------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replica
> tion_copy/annan2024_log.log
  log type:  text
 opened on:  27 Jul 2024, 13:26:30

. 
. ** baseline data prep
. do "$do_loc/_basel-Commands_Test_goodstuff" // generate Mkt_fieldData_census

. /*
> Title: 
> 
> Input:
>         - data-Mgt/Stats?/_M_all_2_18.dta (merged raw data: M?+_M1)
>         - data-Mgt/Stats?/_CM_all_2_18.dta (merged raw data: CM?+_CM1)
>         
> Output:
>         Data:
>                 - Mkt_fieldData.dta/csv
>                 - Mkt_fieldData_census.dta
>                 
>         Graphs:
>                 - FFPhone in 2020/_impact-evaluation/ai_customerVsvendor_gra
> ph.eps
>                 - FFPhone in 2020/_impact-evaluation/mispercep_misconduct_gr
> aph.eps
>                 - _dailyNobCustomers.eps
>                 - _dailyTotMoney.eps
>                 - _dailyNobCustomers_NonM.eps
>                 - _dailyTotMoney_nonM.eps
>                 - _wklyNobUsage.eps
>                 - _wklyTotUseVol.eps
>                 - _wklyNobUsage_nonM.eps
>                 - _wklyTotUseVol_nonM.eps
>                 - _xdevsKdensStr.eps
>                 - _xdevsKdensAsy.eps
>                         
> */
. 
. 
. *TROUBLE FOR VENDORS UP?
. use "$dta_loc_repl/00_raw_anon/_M_all_2_18_corrected.dta", clear 

. 
. tab vendor, miss

 Vendorcode |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |          4        1.02        1.02
          1 |        101       25.70       26.72
          2 |         76       19.34       46.06
          3 |         51       12.98       59.03
          4 |         31        7.89       66.92
          5 |         23        5.85       72.77
          6 |         19        4.83       77.61
          7 |         17        4.33       81.93
          8 |         13        3.31       85.24
          9 |         11        2.80       88.04
         10 |          5        1.27       89.31
         11 |          4        1.02       90.33
         12 |          4        1.02       91.35
         13 |          3        0.76       92.11
         14 |          3        0.76       92.88
         15 |          2        0.51       93.38
         16 |          2        0.51       93.89
         17 |          2        0.51       94.40
         18 |          2        0.51       94.91
         19 |          2        0.51       95.42
         20 |          2        0.51       95.93
         21 |          2        0.51       96.44
         22 |          2        0.51       96.95
         23 |          2        0.51       97.46
         24 |          1        0.25       97.71
         25 |          1        0.25       97.96
         26 |          1        0.25       98.22
         27 |          1        0.25       98.47
         28 |          1        0.25       98.73
         29 |          1        0.25       98.98
         30 |          1        0.25       99.24
         31 |          1        0.25       99.49
         32 |          1        0.25       99.75
         33 |          1        0.25      100.00
------------+-----------------------------------
      Total |        393      100.00

. 
. 
. **number M per local?
. bys ge02: gen MktPerLocal = _N

. hist MktPerLocal
(bin=19, start=1, width=.57894737)

. sum MktPerLocal // 1 to 12 with avg=5 merchants

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
 MktPerLocal |        393    4.689567    2.919903          1         12

. 
. **Next, add customers?
. gen locality_name= ln
(3 missing values generated)

. gen vendor_id= vendor // only unique within loccode (locality)

. gen interviewer =interviewer_v

. 
. merge 1:m distcode ge03 using "$dta_loc_repl/00_raw_anon/_CM_all_2_18.dta" /
> / distcode drops three vendors
(variable locality_name was str3, now str40 to accommodate using data's
       values)
(label _merge already defined)

    Result                      Number of obs
    -----------------------------------------
    Not matched                           133
        from master                        56  (_merge==1)
        from using                         77  (_merge==2)

    Matched                             1,921  (_merge==3)
    -----------------------------------------

. 
. 
. *keep if (_merge==3)
. egen Mkt = group(loccode vendor_id)

. tab Mkt

group(locco |
de_sampling |
 vendor_id) |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         12        0.58        0.58
          2 |         12        0.58        1.17
          3 |         12        0.58        1.75
          4 |         12        0.58        2.34
          5 |         18        0.88        3.21
          6 |         13        0.63        3.85
          7 |          1        0.05        3.89
          8 |         23        1.12        5.01
          9 |         12        0.58        5.60
         10 |         12        0.58        6.18
         11 |         12        0.58        6.77
         12 |          1        0.05        6.82
         13 |          1        0.05        6.86
         14 |         23        1.12        7.98
         15 |         11        0.54        8.52
         16 |         14        0.68        9.20
         17 |          1        0.05        9.25
         18 |         12        0.58        9.83
         19 |          1        0.05        9.88
         20 |          1        0.05        9.93
         21 |          1        0.05        9.98
         22 |         17        0.83       10.81
         23 |         14        0.68       11.49
         24 |         12        0.58       12.07
         25 |          1        0.05       12.12
         26 |          1        0.05       12.17
         27 |          1        0.05       12.22
         28 |          1        0.05       12.27
         29 |          5        0.24       12.51
         30 |          1        0.05       12.56
         31 |          1        0.05       12.61
         32 |          1        0.05       12.66
         33 |         16        0.78       13.44
         34 |          1        0.05       13.49
         35 |          4        0.19       13.68
         36 |          8        0.39       14.07
         37 |          7        0.34       14.41
         38 |          1        0.05       14.46
         39 |          6        0.29       14.75
         40 |          4        0.19       14.95
         41 |          5        0.24       15.19
         42 |          6        0.29       15.48
         43 |         12        0.58       16.07
         44 |          1        0.05       16.11
         45 |          1        0.05       16.16
         46 |          5        0.24       16.41
         47 |          6        0.29       16.70
         48 |          2        0.10       16.80
         49 |          8        0.39       17.19
         50 |          2        0.10       17.28
         51 |          5        0.24       17.53
         52 |          5        0.24       17.77
         53 |          9        0.44       18.21
         54 |          3        0.15       18.35
         55 |          6        0.29       18.65
         56 |         13        0.63       19.28
         57 |          6        0.29       19.57
         58 |         20        0.97       20.55
         59 |          7        0.34       20.89
         60 |          4        0.19       21.08
         61 |          5        0.24       21.32
         62 |          1        0.05       21.37
         63 |          5        0.24       21.62
         64 |          2        0.10       21.71
         65 |          3        0.15       21.86
         66 |         12        0.58       22.44
         67 |          1        0.05       22.49
         68 |          1        0.05       22.54
         69 |          1        0.05       22.59
         70 |          1        0.05       22.64
         71 |          1        0.05       22.69
         72 |          2        0.10       22.78
         73 |          1        0.05       22.83
         74 |          1        0.05       22.88
         75 |          1        0.05       22.93
         76 |          1        0.05       22.98
         77 |          1        0.05       23.03
         78 |          1        0.05       23.08
         79 |          1        0.05       23.13
         80 |          1        0.05       23.17
         81 |          2        0.10       23.27
         82 |          1        0.05       23.32
         83 |          1        0.05       23.37
         84 |          3        0.15       23.52
         85 |          3        0.15       23.66
         86 |          4        0.19       23.86
         87 |          1        0.05       23.90
         88 |         12        0.58       24.49
         89 |          1        0.05       24.54
         90 |          2        0.10       24.63
         91 |          4        0.19       24.83
         92 |          6        0.29       25.12
         93 |          1        0.05       25.17
         94 |          2        0.10       25.27
         95 |          1        0.05       25.32
         96 |          3        0.15       25.46
         97 |          3        0.15       25.61
         98 |          2        0.10       25.71
         99 |          1        0.05       25.75
        100 |          2        0.10       25.85
        101 |          2        0.10       25.95
        102 |          3        0.15       26.10
        103 |          1        0.05       26.14
        104 |          1        0.05       26.19
        105 |          4        0.19       26.39
        106 |          1        0.05       26.44
        107 |          3        0.15       26.58
        108 |          2        0.10       26.68
        109 |          3        0.15       26.83
        110 |          2        0.10       26.92
        111 |          2        0.10       27.02
        112 |          3        0.15       27.17
        113 |          1        0.05       27.22
        114 |          1        0.05       27.26
        115 |          1        0.05       27.31
        116 |          3        0.15       27.46
        117 |          3        0.15       27.60
        118 |          1        0.05       27.65
        119 |          3        0.15       27.80
        120 |          2        0.10       27.90
        121 |          2        0.10       27.99
        122 |          2        0.10       28.09
        123 |          2        0.10       28.19
        124 |          1        0.05       28.24
        125 |          1        0.05       28.29
        126 |          1        0.05       28.33
        127 |          2        0.10       28.43
        128 |          2        0.10       28.53
        129 |          3        0.15       28.68
        130 |          1        0.05       28.72
        131 |          2        0.10       28.82
        132 |          4        0.19       29.02
        133 |          3        0.15       29.16
        134 |          1        0.05       29.21
        135 |          5        0.24       29.45
        136 |         12        0.58       30.04
        137 |          4        0.19       30.23
        138 |          7        0.34       30.57
        139 |          3        0.15       30.72
        140 |          5        0.24       30.96
        141 |          2        0.10       31.06
        142 |          1        0.05       31.11
        143 |          2        0.10       31.21
        144 |          5        0.24       31.45
        145 |          1        0.05       31.50
        146 |          5        0.24       31.74
        147 |          7        0.34       32.08
        148 |         11        0.54       32.62
        149 |          5        0.24       32.86
        150 |         18        0.88       33.74
        151 |          1        0.05       33.79
        152 |          2        0.10       33.89
        153 |          3        0.15       34.03
        154 |          4        0.19       34.23
        155 |         12        0.58       34.81
        156 |         18        0.88       35.69
        157 |          2        0.10       35.78
        158 |          1        0.05       35.83
        159 |          2        0.10       35.93
        160 |          2        0.10       36.03
        161 |          1        0.05       36.08
        162 |          3        0.15       36.22
        163 |          2        0.10       36.32
        164 |          3        0.15       36.47
        165 |         11        0.54       37.00
        166 |         11        0.54       37.54
        167 |         14        0.68       38.22
        168 |          7        0.34       38.56
        169 |          7        0.34       38.90
        170 |         17        0.83       39.73
        171 |         21        1.02       40.75
        172 |         23        1.12       41.87
        173 |          3        0.15       42.02
        174 |          6        0.29       42.31
        175 |          6        0.29       42.60
        176 |          3        0.15       42.75
        177 |          6        0.29       43.04
        178 |          6        0.29       43.33
        179 |          6        0.29       43.62
        180 |          3        0.15       43.77
        181 |          6        0.29       44.06
        182 |          9        0.44       44.50
        183 |          4        0.19       44.69
        184 |          5        0.24       44.94
        185 |          6        0.29       45.23
        186 |          9        0.44       45.67
        187 |          7        0.34       46.01
        188 |         10        0.49       46.49
        189 |          6        0.29       46.79
        190 |          6        0.29       47.08
        191 |          6        0.29       47.37
        192 |          6        0.29       47.66
        193 |          3        0.15       47.81
        194 |          2        0.10       47.91
        195 |          3        0.15       48.05
        196 |          3        0.15       48.20
        197 |          6        0.29       48.49
        198 |          6        0.29       48.78
        199 |          6        0.29       49.07
        200 |          9        0.44       49.51
        201 |          9        0.44       49.95
        202 |          6        0.29       50.24
        203 |         12        0.58       50.83
        204 |         10        0.49       51.31
        205 |          1        0.05       51.36
        206 |          2        0.10       51.46
        207 |          4        0.19       51.66
        208 |          2        0.10       51.75
        209 |          1        0.05       51.80
        210 |          5        0.24       52.04
        211 |          2        0.10       52.14
        212 |          1        0.05       52.19
        213 |          2        0.10       52.29
        214 |          1        0.05       52.34
        215 |          2        0.10       52.43
        216 |          1        0.05       52.48
        217 |          1        0.05       52.53
        218 |          2        0.10       52.63
        219 |          1        0.05       52.68
        220 |          2        0.10       52.78
        221 |          3        0.15       52.92
        222 |          1        0.05       52.97
        223 |          4        0.19       53.16
        224 |          4        0.19       53.36
        225 |          3        0.15       53.51
        226 |          2        0.10       53.60
        227 |          6        0.29       53.89
        228 |          2        0.10       53.99
        229 |          3        0.15       54.14
        230 |          6        0.29       54.43
        231 |          2        0.10       54.53
        232 |          2        0.10       54.63
        233 |          3        0.15       54.77
        234 |          1        0.05       54.82
        235 |          1        0.05       54.87
        236 |          3        0.15       55.01
        237 |          4        0.19       55.21
        238 |          3        0.15       55.36
        239 |          2        0.10       55.45
        240 |          5        0.24       55.70
        241 |         12        0.58       56.28
        242 |         12        0.58       56.86
        243 |         12        0.58       57.45
        244 |          2        0.10       57.55
        245 |          2        0.10       57.64
        246 |          2        0.10       57.74
        247 |         12        0.58       58.33
        248 |          2        0.10       58.42
        249 |          2        0.10       58.52
        250 |          2        0.10       58.62
        251 |          1        0.05       58.67
        252 |          6        0.29       58.96
        253 |          4        0.19       59.15
        254 |          1        0.05       59.20
        255 |          1        0.05       59.25
        256 |          1        0.05       59.30
        257 |          1        0.05       59.35
        258 |          1        0.05       59.40
        259 |          2        0.10       59.49
        260 |          1        0.05       59.54
        261 |          2        0.10       59.64
        262 |          3        0.15       59.79
        263 |          3        0.15       59.93
        264 |          1        0.05       59.98
        265 |          3        0.15       60.13
        266 |          2        0.10       60.22
        267 |          1        0.05       60.27
        268 |          1        0.05       60.32
        269 |          1        0.05       60.37
        270 |          1        0.05       60.42
        271 |          1        0.05       60.47
        272 |          4        0.19       60.66
        273 |          1        0.05       60.71
        274 |          4        0.19       60.91
        275 |          3        0.15       61.05
        276 |         12        0.58       61.64
        277 |          2        0.10       61.73
        278 |         15        0.73       62.46
        279 |          3        0.15       62.61
        280 |          1        0.05       62.66
        281 |          2        0.10       62.76
        282 |          2        0.10       62.85
        283 |          2        0.10       62.95
        284 |          2        0.10       63.05
        285 |          2        0.10       63.15
        286 |          2        0.10       63.24
        287 |          6        0.29       63.53
        288 |          2        0.10       63.63
        289 |          1        0.05       63.68
        290 |          1        0.05       63.73
        291 |         16        0.78       64.51
        292 |          9        0.44       64.95
        293 |          3        0.15       65.09
        294 |          1        0.05       65.14
        295 |          1        0.05       65.19
        296 |          1        0.05       65.24
        297 |          1        0.05       65.29
        298 |         15        0.73       66.02
        299 |          4        0.19       66.21
        300 |          1        0.05       66.26
        301 |          1        0.05       66.31
        302 |         13        0.63       66.94
        303 |         12        0.58       67.53
        304 |          2        0.10       67.62
        305 |          1        0.05       67.67
        306 |          2        0.10       67.77
        307 |          1        0.05       67.82
        308 |          7        0.34       68.16
        309 |         11        0.54       68.70
        310 |          9        0.44       69.13
        311 |         12        0.58       69.72
        312 |          3        0.15       69.86
        313 |          2        0.10       69.96
        314 |         13        0.63       70.59
        315 |          4        0.19       70.79
        316 |          5        0.24       71.03
        317 |          4        0.19       71.23
        318 |         13        0.63       71.86
        319 |         11        0.54       72.40
        320 |          2        0.10       72.49
        321 |          4        0.19       72.69
        322 |          3        0.15       72.83
        323 |          4        0.19       73.03
        324 |          2        0.10       73.13
        325 |         13        0.63       73.76
        326 |         17        0.83       74.59
        327 |         12        0.58       75.17
        328 |         13        0.63       75.80
        329 |          1        0.05       75.85
        330 |          9        0.44       76.29
        331 |         15        0.73       77.02
        332 |         11        0.54       77.56
        333 |          9        0.44       77.99
        334 |         12        0.58       78.58
        335 |          1        0.05       78.63
        336 |          6        0.29       78.92
        337 |          1        0.05       78.97
        338 |          1        0.05       79.02
        339 |          2        0.10       79.11
        340 |          1        0.05       79.16
        341 |          4        0.19       79.36
        342 |          1        0.05       79.41
        343 |          3        0.15       79.55
        344 |          1        0.05       79.60
        345 |          1        0.05       79.65
        346 |          2        0.10       79.75
        347 |          1        0.05       79.80
        348 |          1        0.05       79.84
        349 |         12        0.58       80.43
        350 |         12        0.58       81.01
        351 |          8        0.39       81.40
        352 |          1        0.05       81.45
        353 |          1        0.05       81.50
        354 |          1        0.05       81.55
        355 |          5        0.24       81.79
        356 |          3        0.15       81.94
        357 |          1        0.05       81.99
        358 |         12        0.58       82.57
        359 |          1        0.05       82.62
        360 |         25        1.22       83.84
        361 |          1        0.05       83.89
        362 |         18        0.88       84.76
        363 |          1        0.05       84.81
        364 |         13        0.63       85.44
        365 |         12        0.58       86.03
        366 |         17        0.83       86.85
        367 |         15        0.73       87.59
        368 |          4        0.19       87.78
        369 |          1        0.05       87.83
        370 |          1        0.05       87.88
        371 |          1        0.05       87.93
        372 |          1        0.05       87.97
        373 |         21        1.02       89.00
        374 |          1        0.05       89.05
        375 |          1        0.05       89.09
        376 |          8        0.39       89.48
        377 |         15        0.73       90.21
        378 |         13        0.63       90.85
        379 |          1        0.05       90.90
        380 |         23        1.12       92.02
        381 |         20        0.97       92.99
        382 |          1        0.05       93.04
        383 |          5        0.24       93.28
        384 |          6        0.29       93.57
        385 |          6        0.29       93.87
        386 |         15        0.73       94.60
        387 |          9        0.44       95.03
        388 |          7        0.34       95.37
        389 |          4        0.19       95.57
        390 |         15        0.73       96.30
        391 |         15        0.73       97.03
        392 |          2        0.10       97.13
        393 |          7        0.34       97.47
        394 |          3        0.15       97.61
        395 |          1        0.05       97.66
        396 |          1        0.05       97.71
        397 |          1        0.05       97.76
        398 |          9        0.44       98.20
        399 |          2        0.10       98.30
        400 |         14        0.68       98.98
        401 |          4        0.19       99.17
        402 |          3        0.15       99.32
        403 |          4        0.19       99.51
        404 |          1        0.05       99.56
        405 |          5        0.24       99.81
        406 |          1        0.05       99.85
        407 |          2        0.10       99.95
        408 |          1        0.05      100.00
------------+-----------------------------------
      Total |      2,054      100.00

. 
. ** # of localities & # of customers per mkt
. egen cnoofLocalities = group(loccode)

. bys Mkt: gen cnoofCustPerMkt = _N

. 
. sum cnoofLocalities

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
cnoofLocal~s |      2,054    68.38608    42.11065          1        137

. sum cnoofCustPerMkt

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
cnoofCustP~t |      2,054    10.17527    6.251776          1         25

. 
. 
. **summaries
. **get customers
. gen cfemale=(c1q1==2)

. replace cfemale=. if missing(c1q1)
(58 real changes made, 58 to missing)

. 
. gen cakan =(c1q2==1)

. replace cakan=. if missing(c1q2)
(58 real changes made, 58 to missing)

. 
. gen cmarried=(c1q3==1)

. replace cmarried=. if missing(c1q3)
(58 real changes made, 58 to missing)

. 
. gen cage =c1q4
(58 missing values generated)

. replace cage=. if missing(c1q4)
(0 real changes made)

. 
. gen cEducAny =(c1q5>1)

. replace cEducAny=. if missing(c1q5)
(58 real changes made, 58 to missing)

. 
. gen cEduc =c1q5
(58 missing values generated)

. replace cEduc=. if missing(c1q5)
(0 real changes made)

. 
. gen cselfemployed =(c1q6==1)

. replace cselfemployed=. if missing(c1q6)
(58 real changes made, 58 to missing)

. 
. gen cselfIncome =c1q7
(58 missing values generated)

. replace cselfIncome=. if missing(c1q7)
(0 real changes made)

. 
. gen cMMoneyregistered=(c1q9==1)

. replace cMMoneyregistered=. if missing(c1q9)
(58 real changes made, 58 to missing)

. 
. 
. **get merchants
. gen mfemale=(m1q1==2)

. replace mfemale=. if missing(m1q1)
(107 real changes made, 107 to missing)

. 
. gen makan =(m1q2==1)

. replace makan=. if missing(m1q2)
(107 real changes made, 107 to missing)

. 
. gen mmarried=(m1q3==1)

. replace mmarried=. if missing(m1q3)
(107 real changes made, 107 to missing)

. 
. gen mage =m1q4
(107 missing values generated)

. replace mage=. if missing(m1q4)
(0 real changes made)

. 
. gen mEducAny =(m1q5>3)

. replace mEducAny=. if missing(m1q5)
(107 real changes made, 107 to missing)

. 
. gen mEduc =m1q5
(107 missing values generated)

. replace mEduc=. if missing(m1q5)
(0 real changes made)

. 
. gen mselfemployed =(m1q6==1)

. replace mselfemployed=. if missing(m1q6)
(107 real changes made, 107 to missing)

. 
. gen mselfIncome =m1q7
(1,118 missing values generated)

. replace mselfIncome=. if missing(m1q7)
(0 real changes made)

. 
. gen mbusTrained = (m2q2==1)

. replace mbusTrained=. if missing(m2q2)
(108 real changes made, 108 to missing)

. 
. **females?
. tab mfemale

    mfemale |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,176       60.40       60.40
          1 |        771       39.60      100.00
------------+-----------------------------------
      Total |      1,947      100.00

. *joint business structure?
. tab m3q1

  Currently |
     do you |
offer other |
services at |
       your |
   business |
   center , |
 other than |
      M-Mon |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      1,463       75.18       75.18
          2 |        483       24.82      100.00
------------+-----------------------------------
      Total |      1,946      100.00

. 
. 
. **Fraud: Measure I
. gen cfAttempts =(c5q7a==1 | c5q7b==1 | c5q7c==1)

. replace cfAttempts=. if missing(c5q7a)
(59 real changes made, 59 to missing)

. replace cfAttempts=. if missing(c5q7b)
(0 real changes made)

. replace cfAttempts=. if missing(c5q7c)
(0 real changes made)

. 
. gen cfAccountUse =(c5q7a==1)

. replace cfAccountUse=. if missing(c5q7a)
(59 real changes made, 59 to missing)

. 
. gen cfCallers =(c5q7b==1)

. replace cfAccountUse=. if missing(c5q7b)
(0 real changes made)

. 
. gen cfIncorrects =(c5q7c==1)

. replace cfIncorrects=. if missing(c5q7c)
(59 real changes made, 59 to missing)

. 
. sum cfAttempts cfAccountUse cfCallers cfIncorrects

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
  cfAttempts |      1,995    .5854637    .4927653          0          1
cfAccountUse |      1,995    .1313283    .3378438          0          1
   cfCallers |      2,054    .5063291    .5000817          0          1
cfIncorrects |      1,995    .1368421    .3437668          0          1

. 
. **xbase correlates of fraud
. reg cfAttempts cfemale cakan cmarried cage cEducAny cselfemployed cselfIncom
> e cMMoneyregistered, cluster(loccode)

Linear regression                               Number of obs     =      1,995
                                                F(8, 136)         =      17.14
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0894
                                                Root MSE          =     .47117

                     (Std. err. adjusted for 137 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
  cfAttempts | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     cfemale |   -.009025    .026135    -0.35   0.730    -.0607086    .0426585
       cakan |   .0732728   .0299893     2.44   0.016     .0139671    .1325785
    cmarried |   -.033131   .0251229    -1.32   0.189     -.082813     .016551
        cage |  -.0026768   .0008422    -3.18   0.002    -.0043423   -.0010113
    cEducAny |  -.0184873   .0396772    -0.47   0.642    -.0969514    .0599767
cselfemplo~d |   .0896694   .0256751     3.49   0.001     .0388952    .1404435
 cselfIncome |   .0959474    .015543     6.17   0.000     .0652102    .1266846
cMMoneyreg~d |   .3248741   .0437956     7.42   0.000     .2382657    .4114826
       _cons |   .1992806   .0794959     2.51   0.013     .0420727    .3564886
------------------------------------------------------------------------------

. 
. 
. 
. **Knowledge discrepancies & perceived Mkt structure/ fraud evidence?
. *Knowledge test?
. **Customers?
. **c8q1b=c200 vs c8q2=c1200
. gen c_chargeC200 = c8q1b
(1,004 missing values generated)

. 
. replace c_chargeC200=. if (c_chargeC200==0 | c_chargeC200>=99)
(65 real changes made, 65 to missing)

. // br c_chargeC200
. 
. *replace c_chargeC200=. if (c_chargeC200==0 | c_chargeC200==99)
. *hist c_chargeC200, xline(2, lwidth(vthick) lcolor(blue)) fcolor(none) title
> ("Knowledge Test: Customers, MTN Charge for GHC200") ///
> * xtitle("Discrepancy in stated charges for GHC200") text(2 2 "Correct charg
> e--in blue", place(e))
. 
. gen c_x200=c_chargeC200-2
(1,069 missing values generated)

. *hist c_x200
. 
. gen c_chargeC1200 = c8q2
(59 missing values generated)

. replace c_chargeC1200=. if (c_chargeC1200==0 | c_chargeC1200>=99)
(391 real changes made, 391 to missing)

. *replace c_chargeC1200=. if (c_chargeC1200==0 | c_chargeC1200==99)
. *hist c_chargeC1200, xline(10, lwidth(vthick) lcolor(blue)) fcolor(none) tit
> le("Knowledge Test: Customers, MTN Charge for GHC1200") ///
> * xtitle("Discrepancy in stated charges for GHC1200") text(0.15 10 "Correct 
> charge--in blue", place(e))
. 
. gen c_x1200=c_chargeC1200-10
(450 missing values generated)

. *hist c_x1200
. 
. gen c_deviations = c_x200
(1,069 missing values generated)

. replace c_deviations= c_x1200 if missing(c_deviations)
(851 real changes made)

. // hist c_deviations
. 
. **gender difference in customer knowledge?
. reg c_deviations cfemale

      Source |       SS           df       MS      Number of obs   =     1,836
-------------+----------------------------------   F(1, 1834)      =      0.81
       Model |  10.5657921         1  10.5657921   Prob > F        =    0.3677
    Residual |  23874.0938     1,834  13.0174993   R-squared       =    0.0004
-------------+----------------------------------   Adj R-squared   =   -0.0001
       Total |  23884.6596     1,835  13.0161633   Root MSE        =     3.608

------------------------------------------------------------------------------
c_deviations | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     cfemale |   .1562315   .1734129     0.90   0.368    -.1838759    .4963389
       _cons |   1.213162   .1364662     8.89   0.000     .9455161    1.480807
------------------------------------------------------------------------------

. 
. *drop if missing(c_deviations)
. *drop if missing(cfemale)
. cdfplot c_deviations, by(cfemale) opt1(lc(blue red)) xtitle("Knowledge Tests
> : n (Males)=231, n (Females)=157") ytitle("CDF") legend(pos(3) col(1) stack 
> label(1 "Males") label(2 "Females"))
(0 observations deleted)

. hist c_deviations, by(cfemale)

. 
. gen c_correctsI=(c_deviations==0) 

. gen c_corrects=(c_deviations==0)  if !missing(c_deviations)
(218 missing values generated)

. bys cfemale: sum c_corrects

------------------------------------------------------------------------------
-> cfemale = 0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
  c_corrects |        699    .5207439    .4999272          0          1

------------------------------------------------------------------------------
-> cfemale = 1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
  c_corrects |      1,137    .4687775    .4992438          0          1

------------------------------------------------------------------------------
-> cfemale = .

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
  c_corrects |          0


. bys loccode cfemale: egen fq_cc_corrects = mean(c_corrects) 
(58 missing values generated)

. cdfplot fq_cc_corrects if !missing(cfemale), by(cfemale) opt1(lc(blue red)) 
> xtitle("Knowledge Tests: n (Males)=743, n (Females)=1,253") ytitle("CDF") le
> gend(pos(3) col(1) stack label(1 "Males") label(2 "Females"))
(0 observations deleted)

. 
. regress c_corrects cfemale

      Source |       SS           df       MS      Number of obs   =     1,836
-------------+----------------------------------   F(1, 1834)      =      4.69
       Model |  1.16899006         1  1.16899006   Prob > F        =    0.0306
    Residual |  457.590814     1,834  .249504261   R-squared       =    0.0025
-------------+----------------------------------   Adj R-squared   =    0.0020
       Total |  458.759804     1,835  .250005343   Root MSE        =     .4995

------------------------------------------------------------------------------
  c_corrects | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     cfemale |  -.0519664    .024008    -2.16   0.031    -.0990524   -.0048805
       _cons |   .5207439    .018893    27.56   0.000     .4836899    .5577979
------------------------------------------------------------------------------

. regress fq_cc_corrects cfemale

      Source |       SS           df       MS      Number of obs   =     1,996
-------------+----------------------------------   F(1, 1994)      =     16.72
       Model |  1.05156022         1  1.05156022   Prob > F        =    0.0000
    Residual |  125.434993     1,994  .062906215   R-squared       =    0.0083
-------------+----------------------------------   Adj R-squared   =    0.0078
       Total |  126.486553     1,995  .063401781   Root MSE        =    .25081

------------------------------------------------------------------------------
fq_cc_corr~s | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     cfemale |  -.0474818   .0116133    -4.09   0.000    -.0702574   -.0247063
       _cons |    .517694   .0092014    56.26   0.000     .4996487    .5357393
------------------------------------------------------------------------------

. **42%(c-females) vs 48%(c-males) accuracy
. 
. 
. 
. **Merchants?
. gen m_chargeC200 = m6q1b
(1,123 missing values generated)

. replace m_chargeC200=. if (m_chargeC200==0 | m_chargeC200>=99)
(6 real changes made, 6 to missing)

. *replace m_chargeC200=. if (m_chargeC200==0 | m_chargeC200==99)
. *hist m_chargeC200, xline(2, lwidth(vthick) lcolor(blue)) fcolor(none) title
> ("Knowledge Test: Merchants, MTN Charge for GHC200") ///
> * xtitle("Discrepancy in stated charges for GHC200") text(2 2 "Correct charg
> e--in blue", place(e))
. 
. gen m_x200=m_chargeC200-2
(1,129 missing values generated)

. *hist m_x200
. 
. gen m_chargeC1200 = m6q2
(108 missing values generated)

. replace m_chargeC1200=. if (m_chargeC1200==0 | m_chargeC1200>=99)
(156 real changes made, 156 to missing)

. *replace m_chargeC1200=. if (m_chargeC1200==0 | m_chargeC1200==99)
. *hist m_chargeC1200, xline(2, lwidth(vthick) lcolor(blue)) fcolor(none) titl
> e("Knowledge Test: Merchants, MTN Charge for GHC1200") ///
> * xtitle("Discrepancy in stated charges for GHC1200") text(2 10 "Correct cha
> rge--in blue", place(e))
. 
. gen m_x1200=m_chargeC1200-10
(264 missing values generated)

. *hist m_x1200 if m_x1200<20
. 
. gen m_deviations = m_x200
(1,129 missing values generated)

. replace m_deviations= m_x1200 if missing(m_deviations)
(961 real changes made)

. hist m_deviations
(bin=32, start=-5, width=.59375)

. 
. regress m_deviations mfemale

      Source |       SS           df       MS      Number of obs   =     1,886
-------------+----------------------------------   F(1, 1884)      =      7.08
       Model |  28.8840135         1  28.8840135   Prob > F        =    0.0079
    Residual |  7689.04865     1,884  4.08123601   R-squared       =    0.0037
-------------+----------------------------------   Adj R-squared   =    0.0032
       Total |  7717.93266     1,885  4.09439398   Root MSE        =    2.0202

------------------------------------------------------------------------------
m_deviations | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     mfemale |    .252038     .09474     2.66   0.008     .0662318    .4378443
       _cons |   .7466548   .0603383    12.37   0.000     .6283179    .8649917
------------------------------------------------------------------------------

. 
. 
. **Testing AI?
. twoway (hist c_x200 if c_x200<200, color(green)) ///
> (hist m_x200 if m_x200<200, fcolor(green) color(blue)), legend(order(1 "Cust
> omers" 2 "Merchants" ))

. graph export "$output_loc/baseline/_x200.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/baseline/_x200.eps saved as EPS format

. 
. 
. twoway (hist c_x1200 if c_x1200<200, color(green)) ///
> (hist m_x1200 if m_x1200<200, fcolor(grey) color(blue)), legend(order(1 "Cus
> tomers" 2 "Merchants" ))
(note:  named style grey not found in class color, default attributes used)

. graph export "$output_loc/baseline/_x1200.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/baseline/_x1200.eps saved as EPS format

. 
. *replace _asymLocally1200=. if (c_x1200<-800 | c_x1200>800)
. twoway (hist c_deviations if c_deviations<200, color(green)) ///
> (hist m_deviations if m_deviations<200, fcolor(grey) color(blue)), legend(or
> der(1 "Customers" 2 "Merchants" )) title("Knowledge Tests:") subtitle("Devia
> tions from Correct Transactional Charges") note("NOTE: Customers are 52.3% o
> f the time Incorrect. Merchants are 33.1% of the time Incorrect")
(note:  named style grey not found in class color, default attributes used)

. graph export "$output_loc/baseline/_xdevs.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/baseline/_xdevs.eps saved as EPS format

. 
. 
. **incorrections Counts...
. count if (c_deviations==0 & c_deviations<200)
  897

. count if (!missing(c_deviations) & c_deviations<200)
  1,836

. dis "Wrong crate is, custormers: =" (1-(897/1836))*100 "%"
Wrong crate is, custormers: =51.143791%

. 
. count if (m_deviations==0 & m_deviations<200)
  1,225

. count if (!missing(m_deviations) & m_deviations<200)
  1,886

. dis "Wrong mrate is, merchants: =" (1-(1225/1886))*100 "%"
Wrong mrate is, merchants: =35.04772%

. 
. **incorrectness: 51% vs 35%
. 
. 
. 
. **by Gender?
. gen m_correctsI=(m_deviations==0)

. gen m_corrects=(m_deviations==0) if !missing(m_deviations)
(168 missing values generated)

. 
. bys mfemale: sum m_corrects

------------------------------------------------------------------------------
-> mfemale = 0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
  m_corrects |      1,121    .6958073      .46027          0          1

------------------------------------------------------------------------------
-> mfemale = 1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
  m_corrects |        765    .5816993    .4936028          0          1

------------------------------------------------------------------------------
-> mfemale = .

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
  m_corrects |          0


. regress m_corrects mfemale

      Source |       SS           df       MS      Number of obs   =     1,886
-------------+----------------------------------   F(1, 1884)      =     26.34
       Model |  5.92048529         1  5.92048529   Prob > F        =    0.0000
    Residual |  423.414085     1,884  .224742083   R-squared       =    0.0138
-------------+----------------------------------   Adj R-squared   =    0.0133
       Total |  429.334571     1,885  .227763698   Root MSE        =    .47407

------------------------------------------------------------------------------
  m_corrects | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     mfemale |   -.114108   .0222321    -5.13   0.000      -.15771   -.0705059
       _cons |   .6958073   .0141592    49.14   0.000     .6680379    .7235767
------------------------------------------------------------------------------

. regress m_corrects mfemale, cluster(loccode)

Linear regression                               Number of obs     =      1,886
                                                F(1, 129)         =       2.54
                                                Prob > F          =     0.1136
                                                R-squared         =     0.0138
                                                Root MSE          =     .47407

                     (Std. err. adjusted for 130 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
  m_corrects | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     mfemale |   -.114108   .0716351    -1.59   0.114    -.2558397    .0276237
       _cons |   .6958073   .0488952    14.23   0.000      .599067    .7925477
------------------------------------------------------------------------------

. **59(m-females) vs (m-males)70 accuracy
. **graphically?
. bys loccode mfemale: egen fq_mm_corrects = mean(m_corrects)
(157 missing values generated)

. cdfplot fq_mm_corrects if !missing(cfemale), by(cfemale) opt1(lc(blue red)) 
> xtitle("Knowledge Tests: n (Males)=743, n (Females)=1,253") ytitle("CDF") le
> gend(pos(3) col(1) stack label(1 "Males") label(2 "Females"))
(0 observations deleted)

. 
. **ttests
. ttest c_deviations == m_deviations, unpaired

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
c_devi~s |   1,836    1.309913    .0841987    3.607792    1.144778    1.475048
m_devi~s |   1,886    .8488865    .0465934    2.023461    .7575066    .9402665
---------+--------------------------------------------------------------------
Combined |   3,722    1.076303    .0479179    2.923385    .9823551    1.170251
---------+--------------------------------------------------------------------
    diff |            .4610263    .0955588                .2736735    .6483792
------------------------------------------------------------------------------
    diff = mean(c_deviations) - mean(m_deviations)                t =   4.8245
H0: diff = 0                                     Degrees of freedom =     3720

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. 
. 
. ** Figure B.7 --------------------------------------------------------------
> ----
. **Asymmetric Tnformation Test**
. bys loccode: egen mkt_m_corrects = mean(m_corrects)
(106 missing values generated)

. bys loccode: egen mkt_c_corrects = mean(c_corrects)

. 
. 
. bys loccode vendor_id: gen nobvendors=_N

. bys loccode: gen nobcustomers=_N

. 
. sum mkt_c_corrects, d

                       mkt_c_corrects
-------------------------------------------------------------
      Percentiles      Smallest
 1%     .0666667              0
 5%      .173913              0
10%     .2352941              0       Obs               2,054
25%     .3333333              0       Sum of wgt.       2,054

50%     .4285714                      Mean           .4875098
                        Largest       Std. dev.       .222708
75%     .6551724              1
90%           .8              1       Variance       .0495989
95%     .9411765              1       Skewness       .4305963
99%            1              1       Kurtosis       2.627183

. sum mkt_m_corrects if (mkt_m_corrects > 0), d

                       mkt_m_corrects
-------------------------------------------------------------
      Percentiles      Smallest
 1%     .0526316       .0526316
 5%     .1666667       .0526316
10%         .375       .0526316       Obs               1,727
25%           .5       .0526316       Sum of wgt.       1,727

50%     .7931035                      Mean           .7309925
                        Largest       Std. dev.      .2775775
75%            1              1
90%            1              1       Variance       .0770493
95%            1              1       Skewness      -.6152798
99%            1              1       Kurtosis       2.348765

. *Means: c=48 vs v=73
. *Median: c=42 vs v=79
. **Trim: zero vendor knowledge in a whole locality is sugestive of potential 
> vendor misconduct, so drop those
. distplot mkt_c_corrects mkt_m_corrects if (mkt_m_corrects > 0), xline(0.48, 
> lp(solid) lw(vthin)) text(0.8 0.38 "Customers: Overall share", size(vsmall))
>  xline(0.73, lp(dash) lw(vthin)) lp(solid dash) text(0.1 0.82 "Vendors: Over
> all share", size(vsmall))  xtitle("Share with correct answers") ytitle("Cumu
> lative Probability") legend(pos(7) row(1) stack label(1 "Customers") label(2
>  "Vendors"))

. gr export "$output_loc/baseline/ai_customerVsvendor_graph.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/baseline/ai_customerVsvendor_graph.eps saved as EPS format

. **NOTE: Trimmed to exlude unrealistic zero vendor knowlege at the mkt level
. 
. 
. **Misperceived beliefs about Misconduct?
. gen cat="true" if _n==1
(2,053 missing values generated)

. gen misconduct=0.22 if cat=="true"
(2,053 missing values generated)

. gen n=663 if cat=="true"
(2,053 missing values generated)

. gen sd=0.41 if cat=="true"
(2,053 missing values generated)

. 
. replace cat="subjective" if _n==2
variable cat was str4 now str10
(1 real change made)

. replace misconduct=0.59 if cat=="subjective"
(1 real change made)

. replace sd=0.49 if cat=="subjective"
(1 real change made)

. replace n=1921 if cat=="subjective"
(1 real change made)

. 
. gen se=sd/sqrt(n) 
(2,052 missing values generated)

. gen upper = misconduct + se
(2,052 missing values generated)

. gen lower = misconduct - se 
(2,052 missing values generated)

. 
. generate himiscon90 = misconduct + invttail(n-1,0.05)*(sd / sqrt(n))
(2,052 missing values generated)

. generate lowmiscon90 = misconduct - invttail(n-1,0.05)*(sd / sqrt(n))
(2,052 missing values generated)

. *graph twoway (bar meanwrite race) (rcap hiwrite lowrite race), by(se) // (Y
> K: change to SE. meanwrite DNE)
. 
. gen catt=(cat=="true") if !missing(cat)
(2,052 missing values generated)

. 
. ** Figure B.10 -------------------------------------------------------------
> ---
. graph hbar misconduct, over(cat, sort(1)) bar(1, color(black)) bar(2, color(
> gs8)) nofill asyvars ///
>  blabel(group, position(inside) format(%4.2f) box fcolor(white) lcolor(white
> )) ytitle("Misconduct Incidence: Share of transactions overcharged", size(sm
> all)) blabel(bar) ///
>  legend(pos(7) row(1) stack label(1 "Perceived misconduct") label(2 "Objecti
> ve (true) misconduct"))

. gr export "$output_loc/baseline/mispercep_misconduct_graph.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/baseline/mispercep_misconduct_graph.eps saved as EPS format

. 
. ttesti 663 0.22 0.41 1921 0.59 0.49

Two-sample t test with equal variances
------------------------------------------------------------------------------
         |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       x |     663         .22    .0159231         .41    .1887342    .2512658
       y |   1,921         .59    .0111798         .49    .5680743    .6119257
---------+--------------------------------------------------------------------
Combined |   2,584    .4950658    .0097903    .4976719    .4758681    .5142635
---------+--------------------------------------------------------------------
    diff |                -.37    .0212056               -.4115816   -.3284184
------------------------------------------------------------------------------
    diff = mean(x) - mean(y)                                      t = -17.4483
H0: diff = 0                                     Degrees of freedom =     2582

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. 
. 
. 
.   
. **Correlates of incorrectness: Merchants vs Customers
. gen _dVc=(c_deviations !=0)

. gen _dVm=(m_deviations !=0)

. reg _dVc cfemale cakan cmarried cage cEduc cMMoneyregistered cselfemployed c
> selfIncome

      Source |       SS           df       MS      Number of obs   =     1,996
-------------+----------------------------------   F(8, 1987)      =      5.66
       Model |  11.0083957         8  1.37604947   Prob > F        =    0.0000
    Residual |  482.880883     1,987  .243020072   R-squared       =    0.0223
-------------+----------------------------------   Adj R-squared   =    0.0184
       Total |  493.889279     1,995  .247563548   Root MSE        =    .49297

------------------------------------------------------------------------------
        _dVc | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     cfemale |   .0377549   .0239016     1.58   0.114      -.00912    .0846298
       cakan |   .1020218   .0231959     4.40   0.000     .0565309    .1475126
    cmarried |   .0110174   .0234592     0.47   0.639    -.0349899    .0570247
        cage |    .001123   .0008014     1.40   0.161    -.0004488    .0026947
       cEduc |  -.0405209   .0119774    -3.38   0.001    -.0640105   -.0170312
cMMoneyreg~d |   .0082543   .0381308     0.22   0.829    -.0665263    .0830348
cselfemplo~d |  -.0282933   .0252087    -1.12   0.262    -.0777316     .021145
 cselfIncome |  -.0047436   .0134688    -0.35   0.725    -.0311581     .021671
       _cons |   .5545446    .066845     8.30   0.000      .423451    .6856382
------------------------------------------------------------------------------

. reg _dVm mfemale makan mmarried mage mEduc mbusTrained cselfemployed cselfIn
> come

      Source |       SS           df       MS      Number of obs   =     1,889
-------------+----------------------------------   F(8, 1880)      =     17.80
       Model |  31.1088038         8  3.88860047   Prob > F        =    0.0000
    Residual |  410.775791     1,880  .218497761   R-squared       =    0.0704
-------------+----------------------------------   Adj R-squared   =    0.0664
       Total |  441.884595     1,888  .234049044   Root MSE        =    .46744

------------------------------------------------------------------------------
        _dVm | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     mfemale |   .0732953   .0223461     3.28   0.001     .0294696     .117121
       makan |   .0861298   .0218914     3.93   0.000     .0431958    .1290638
    mmarried |   .0860145   .0281889     3.05   0.002     .0307296    .1412994
        mage |  -.0134397   .0015352    -8.75   0.000    -.0164506   -.0104289
       mEduc |  -.0362071   .0142264    -2.55   0.011    -.0641083   -.0083059
 mbusTrained |  -.0040164   .0220315    -0.18   0.855    -.0472251    .0391924
cselfemplo~d |  -.0208333   .0231441    -0.90   0.368    -.0662241    .0245575
 cselfIncome |   -.007813   .0126439    -0.62   0.537    -.0326106    .0169846
       _cons |    .788832   .0605071    13.04   0.000     .6701638    .9075001
------------------------------------------------------------------------------

. 
. *********************************************
. ** YK: where is this reported?
. preserve

.         keep c_deviations m_deviations

.         gen id=_n 

.         tempfile deviations

.         save    `deviations'
file /var/folders/6g/5g5fyd2d2p98vx66fbb08m1r0000gn/T//S_35344.000002 saved
    as .dta format

. 
.         use `deviations', clear

.         keep id c_deviations

.         gen group=0

.         gen deviations=c_deviations
(218 missing values generated)

.         tempfile c_deviations

.         save `c_deviations'
file /var/folders/6g/5g5fyd2d2p98vx66fbb08m1r0000gn/T//S_35344.000003 saved
    as .dta format

. 
.         use `deviations', clear

.         keep id m_deviations

.         gen group=1

.         gen deviations=m_deviations
(168 missing values generated)

.         tempfile m_deviations

.         save    `m_deviations'
file /var/folders/6g/5g5fyd2d2p98vx66fbb08m1r0000gn/T//S_35344.000004 saved
    as .dta format

. 
.         append using `c_deviations'

.         ksmirnov deviations, by(group) //strong nonparametric rejection 1% l
> evel...

Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
---------------------------------------
0                    0.0281       0.231
1                   -0.1329       0.000
Combined K-S         0.1329       0.000

Note: Ties exist in combined dataset;
      there are 28 unique values out of 3722 observations.

. 
. restore

. **********************************************
. 
. **Fraud: Measure II
. gen c_localpFraudi = (c4q17==1)

. replace c_localpFraudi=. if missing(c4q1)
(59 real changes made, 59 to missing)

. 
. gen c_localpFraudii = (c8q3==1)

. replace c_localpFraudii=. if missing(c8q3)
(59 real changes made, 59 to missing)

. 
. gen _clocalpFraud=(c_localpFraudi==1 |c_localpFraudii==1)

. replace _clocalpFraud=. if missing(c_localpFraudi)
(59 real changes made, 59 to missing)

. replace _clocalpFraud=. if missing(c_localpFraudii)
(0 real changes made)

. 
. gen everOvercharged=c_localpFraudi
(59 missing values generated)

. gen thinkOvercharging=c_localpFraudii
(59 missing values generated)

. sum everOvercharged thinkOvercharging _clocalpFraud

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
everOverch~d |      1,995      .20401    .4030774          0          1
thinkOverc~g |      1,995     .435589    .4959582          0          1
_clocalpFr~d |      1,995    .4907268    .5000393          0          1

. 
. 
. **Mkt structure?
. gen c_badReportSys = (c8q4==2)

. replace c_badReportSys=. if missing(c8q4)
(59 real changes made, 59 to missing)

. 
. gen c_dontTrustSys = (c8q5==2)

. replace c_dontTrustSys=. if missing(c8q5)
(59 real changes made, 59 to missing)

. 
. gen c_badMktStructure=(c_badReportSys==1 |c_dontTrustSys==1)

. replace c_badMktStructure=. if missing(c_badReportSys)
(59 real changes made, 59 to missing)

. replace c_badMktStructure=. if missing(c_dontTrustSys)
(0 real changes made)

. 
. sum c_badMktStructure c_badReportSys c_dontTrustSys

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_badMktSt~e |      1,995    .6320802    .4823603          0          1
c_badRepor~s |      1,995    .5969925    .4906252          0          1
c_dontTrus~s |      1,995    .6080201    .4883146          0          1

. *hist c_localpFraud, title("Custmers: Perceived overcharge / fraud")
. *hist c_badMktStructure, discrete fraction gap(5) fcolor(grey) color(blue) t
> itle("Customers: bad Mkt structure to report fraud") 
. 
. 
. 
. ***************************
. **asy info vs mkt str
. gen _cfraud=(cfAttempts==1 | _clocalpFraud==1)

. replace _cfraud=. if missing(cfAttempts)
(59 real changes made, 59 to missing)

. replace _cfraud=. if missing(_clocalpFraud)
(0 real changes made)

. 
. gen _Xcfraud=(cfAccountUse==1 | everOvercharged==1)

. replace _Xcfraud=. if missing(cfAccountUse)
(59 real changes made, 59 to missing)

. replace _Xcfraud=. if missing(everOvercharged)
(0 real changes made)

. 
. 
. gen _asymLocally200 = (c_x200 !=0)  

. replace _asymLocally200=. if missing(c_x200) 
(1,069 real changes made, 1,069 to missing)

. 
. gen _asymLocally1200 = (c_x1200 !=0) 

. replace _asymLocally1200=. if (c_x1200<-800 | c_x1200>800)
(450 real changes made, 450 to missing)

. replace _asymLocally1200=. if missing(c_x1200)  
(0 real changes made)

. 
. gen _asymLocally = (_asymLocally200==1 | _asymLocally1200 ==1)

. replace _asymLocally=. if missing(_asymLocally200)
(1,069 real changes made, 1,069 to missing)

. replace _asymLocally=. if missing(_asymLocally1200)
(232 real changes made, 232 to missing)

. 
. 
. **Testing...
. reg cfAttempts c_badMktStructure _asymLocally, cluster(loccode)

Linear regression                               Number of obs     =        753
                                                F(2, 119)         =       0.08
                                                Prob > F          =     0.9216
                                                R-squared         =     0.0004
                                                Root MSE          =     .49228

                     (Std. err. adjusted for 120 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
  cfAttempts | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
c_badMktSt~e |  -.0214004   .0532427    -0.40   0.688    -.1268263    .0840255
_asymLocally |   .0004103   .0650784     0.01   0.995    -.1284515    .1292721
       _cons |   .6052495   .0656528     9.22   0.000     .4752503    .7352487
------------------------------------------------------------------------------

. reg _Xcfraud c_badMktStructure _asymLocally, cluster(loccode)

Linear regression                               Number of obs     =        753
                                                F(2, 119)         =       3.24
                                                Prob > F          =     0.0425
                                                R-squared         =     0.0144
                                                Root MSE          =     .45474

                     (Std. err. adjusted for 120 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
    _Xcfraud | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
c_badMktSt~e |  -.1090208   .0437161    -2.49   0.014     -.195583   -.0224585
_asymLocally |   .0463847   .0627637     0.74   0.461    -.0778936    .1706631
       _cons |   .3227523   .0663381     4.87   0.000     .1913963    .4541084
------------------------------------------------------------------------------

. 
. 
. **Ia. MMoney sales?
. gen dailyNobCustomers=m2q4a
(108 missing values generated)

. gen dailyTotMoney=m2q4b
(108 missing values generated)

. hist dailyNobCustomers, title(Merchants: dailyNobCustomers)
(bin=32, start=0, width=10.9375)

. graph export "$output_loc/baseline/_dailyNobCustomers.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/baseline/_dailyNobCustomers.eps saved as EPS format

. hist dailyTotMoney, title(Merchants: dailyTotMoney)
(bin=32, start=0, width=1250)

. graph export "$output_loc/baseline/_dailyTotMoney.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/baseline/_dailyTotMoney.eps saved as EPS format

. 
. **Ib. nonMMoney sales?
. gen dailyNobCustomers_nonM =m3q3a1 
(591 missing values generated)

. gen dailyTotMoney_nonM =m3q3a2
(591 missing values generated)

. hist dailyNobCustomers_nonM, title(Merchants: dailyNobCustomers_nonM)
(bin=31, start=0, width=16.129032)

. graph export "$output_loc/baseline/_dailyNobCustomers_NonM.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/baseline/_dailyNobCustomers_NonM.eps saved as EPS format

. hist dailyTotMoney_nonM, title(Merchants: dailyTotMoney_nonM)
(bin=31, start=0, width=29.032258)

. graph export "$output_loc/baseline/_dailyTotMoney_nonM.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/baseline/_dailyTotMoney_nonM.eps saved as EPS format

. 
. 
. **IIa. Take-up & MMoney adoption decisions?
. gen wklyNobUsage=c4q11a
(228 missing values generated)

. gen wklyTotUseVol=c4q11b
(228 missing values generated)

. hist wklyNobUsage, title(Customers: wklyNobUsage)
(bin=32, start=0, width=3.09375)

. graph export "$output_loc/baseline/_wklyNobUsage.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/baseline/_wklyNobUsage.eps saved as EPS format

. hist wklyTotUseVol, title(Customers: wklyTotUseVol)
(bin=32, start=0, width=312.5)

. graph export "$output_loc/baseline/_wklyTotUseVol.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/baseline/_wklyTotUseVol.eps saved as EPS format

. 
. 
. **IIb. Take-up & NonMMoney adoption decisions?
. gen wklyNobUsage_nonM=c4q18a
(59 missing values generated)

. gen wklyTotUseVol_nonM=c4q18b
(59 missing values generated)

. hist wklyNobUsage_nonM, title(Customers: wklyNobUsage_nonM)
(bin=32, start=0, width=15.625)

. graph export "$output_loc/baseline/_wklyNobUsage_nonM.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/baseline/_wklyNobUsage_nonM.eps saved as EPS format

. hist wklyTotUseVol_nonM, title(Customers: wklyTotUseVol_nonM)
(bin=32, start=0, width=625)

. graph export "$output_loc/baseline/_wklyTotUseVol_nonM.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/baseline/_wklyTotUseVol_nonM.eps saved as EPS format

. 
. 
. *IIc. borrow + save behavior?
. gen wklyNobBorrow=c5q2a
(59 missing values generated)

. gen wklyTotBorrowVol=c5q2b
(59 missing values generated)

. gen wklyNobSave=c5q6a
(59 missing values generated)

. gen wklyTotSaveVol=c5q6b
(59 missing values generated)

. sum wklyNobBorrow wklyTotBorrowVol wklyNobSave wklyTotSaveVol

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
wklyNobBor~w |      1,995    5.070677    21.36372          0         99
wklyTotBor~l |      1,995    13.38697    56.55627          0       1000
 wklyNobSave |      1,995    6.227569    34.46038          0        999
wklyTotSav~l |      1,995    51.05113     165.257          0       3000

. 
. 
. 
. **Graphical evidence
. bys Mkt: egen _MktFraudI=mean(cfAttempts)
(55 missing values generated)

. bys Mkt: egen _MktFraudII=mean(_Xcfraud)
(55 missing values generated)

. 
. bys Mkt: egen _MktbadStr=mean(c_badMktStructure)
(55 missing values generated)

. bys Mkt: egen _MktAsym=mean(_asymLocally)
(529 missing values generated)

. 
. *scatter?
. tw (sc _MktFraudI _MktbadStr, jitter(1) xtitle("Market: fraction indicating 
> bad structure") ///
> ytitle("Market: Fraction experiencing attempt fraud")) ///
> (lfit _MktFraudI _MktbadStr if _MktbadStr<=0.5, lcolor(black) lwidth(thick))
>  ///
> (lfit _MktFraudI _MktbadStr if _MktbadStr>=0.5, lcolor(black) lwidth(thick))

. 
. tw (sc _MktFraudI _MktAsym, jitter(1) xtitle("Market: fraction incorrect tra
> nsactional knowledge") ///
> ytitle("Market: Fraction experiencing attempt fraud")) ///
> (lfit _MktFraudI _MktAsym if _MktAsym<=0.5, lcolor(black) lwidth(thick)) ///
> (lfit _MktFraudI _MktAsym if _MktAsym>=0.5, lcolor(black) lwidth(thick))

. 
. *kdensity?
. tw (kdensity _MktFraudI if _MktbadStr==0, lcolor(black) xtitle("Market: Atte
> mpted fraud rate")) ///
> (kdensity _MktFraudI if _MktbadStr==1, lcolor(blue) ytitle("Probability") le
> gend(label(1 "Bad Mkt structure=No") label(2 "Bad Mkt structure=Yes")))

. graph export "$output_loc/baseline/_xdevsKdensStr.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/baseline/_xdevsKdensStr.eps saved as EPS format

. 
. tw (kdensity _MktFraudI if _MktAsym==0, lcolor(black) xtitle("Market:  Attem
> pted fraud rate")) ///
> (kdensity _MktFraudI if _MktAsym==1, lcolor(blue) ytitle("Probability") lege
> nd(label(1 "Incorrect knowledge=No") label(2 "Incorrect knowledge=Yes")))

. graph export "$output_loc/baseline/_xdevsKdensAsy.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/baseline/_xdevsKdensAsy.eps saved as EPS format

. 
. 
. **III. Selection in fraud? any evidence of discrimination, gender?
. reg cfAttempts cfemale cakan cmarried cage cEducAny cMMoneyregistered, clust
> er(loccode)

Linear regression                               Number of obs     =      1,995
                                                F(6, 136)         =      13.10
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0555
                                                Root MSE          =     .47961

                     (Std. err. adjusted for 137 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
  cfAttempts | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     cfemale |  -.0260694   .0273779    -0.95   0.343    -.0802109     .028072
       cakan |   .0765109   .0319051     2.40   0.018     .0134166    .1396052
    cmarried |  -.0134107   .0256181    -0.52   0.601    -.0640721    .0372507
        cage |  -.0021177   .0009054    -2.34   0.021    -.0039083   -.0003272
    cEducAny |  -.0289696   .0427578    -0.68   0.499    -.1135257    .0555866
cMMoneyreg~d |   .3527313   .0449259     7.85   0.000     .2638877    .4415749
       _cons |   .3524268   .0736859     4.78   0.000     .2067086    .4981451
------------------------------------------------------------------------------

. reg _Xcfraud cfemale cakan cmarried cage cEducAny cMMoneyregistered, cluster
> (loccode)

Linear regression                               Number of obs     =      1,995
                                                F(6, 136)         =       8.59
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0255
                                                Root MSE          =     .45063

                     (Std. err. adjusted for 137 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
    _Xcfraud | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     cfemale |   .0207237   .0210711     0.98   0.327    -.0209457    .0623931
       cakan |   .0762447    .025405     3.00   0.003     .0260048    .1264847
    cmarried |   .0169726   .0204661     0.83   0.408    -.0235002    .0574455
        cage |  -.0016851   .0007203    -2.34   0.021    -.0031095   -.0002608
    cEducAny |   .0333856   .0325317     1.03   0.307    -.0309479    .0977191
cMMoneyreg~d |   .1812765    .036329     4.99   0.000     .1094337    .2531192
       _cons |   .0976054   .0577668     1.69   0.093     -.016632    .2118427
------------------------------------------------------------------------------

. 
. **gen mismatches [& sortingX]?
. bys Mkt: gen mismatch_Mktfemale=(cfemale != mfemale)

. bys Mkt: gen mismatch_Mktakan=(cakan != makan)

. bys Mkt: gen _MktEducHighM=(cEducAny < mEducAny)

. 
. reg cfAttempts mismatch_Mktfemale mismatch_Mktakan cmarried cage _MktEducHig
> hM cMMoneyregistered, cluster(loccode)

Linear regression                               Number of obs     =      1,995
                                                F(6, 136)         =      11.83
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0499
                                                Root MSE          =     .48103

                     (Std. err. adjusted for 137 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
  cfAttempts | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
mismatch_M~e |  -.0093327   .0262819    -0.36   0.723    -.0613069    .0426414
mismatch_M~n |   .0124913   .0271171     0.46   0.646    -.0411343    .0661169
    cmarried |  -.0121773   .0259903    -0.47   0.640    -.0635746      .03922
        cage |  -.0017116   .0009421    -1.82   0.071    -.0035748    .0001515
_MktEducHi~M |  -.0006968   .0417546    -0.02   0.987    -.0832691    .0818756
cMMoneyreg~d |   .3549496   .0451931     7.85   0.000     .2655774    .4443218
       _cons |   .3393243   .0633058     5.36   0.000     .2141332    .4645154
------------------------------------------------------------------------------

. reg _Xcfraud mismatch_Mktfemale mismatch_Mktakan cmarried cage _MktEducHighM
>  cMMoneyregistered, cluster(loccode)

Linear regression                               Number of obs     =      1,995
                                                F(6, 136)         =       6.79
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0195
                                                Root MSE          =     .45203

                     (Std. err. adjusted for 137 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
    _Xcfraud | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
mismatch_M~e |   .0233353   .0203768     1.15   0.254     -.016961    .0636316
mismatch_M~n |   .0295322    .023945     1.23   0.220    -.0178204    .0768849
    cmarried |   .0150532   .0208286     0.72   0.471    -.0261367    .0562431
        cage |  -.0014011   .0007397    -1.89   0.060    -.0028639    .0000617
_MktEducHi~M |  -.0393049   .0414349    -0.95   0.345    -.1212449    .0426351
cMMoneyreg~d |   .1837818   .0361555     5.08   0.000     .1122822    .2552814
       _cons |   .1564709   .0515249     3.04   0.003     .0545773    .2583645
------------------------------------------------------------------------------

. 
. 
. **poverty rate, by locality etc? 100% Nat. Pov
. egen c_rScore = rowtotal(c2q1 - c2q10)

. egen m_rScore = rowtotal(m4q1 - m4q10) 

. 
. //customers
. gen c_pov_likelihood = 91.4 if (c_rScore>=0 & c_rScore<=9)
(1,996 missing values generated)

. replace c_pov_likelihood =75.9 if (c_rScore>=10 & c_rScore<=14)
(3 real changes made)

. replace c_pov_likelihood =66.8 if (c_rScore>=15 & c_rScore<=19)
(6 real changes made)

. replace c_pov_likelihood =63.8 if (c_rScore>=20 & c_rScore<=24)
(23 real changes made)

. replace c_pov_likelihood =53.3 if (c_rScore>=25 & c_rScore<=29)
(56 real changes made)

. replace c_pov_likelihood =40.2 if (c_rScore>=30 & c_rScore<=34)
(91 real changes made)

. replace c_pov_likelihood =29.0 if (c_rScore>=35 & c_rScore<=39)
(142 real changes made)

. replace c_pov_likelihood =19.6 if (c_rScore>=40 & c_rScore<=44)
(201 real changes made)

. replace c_pov_likelihood =11.7 if (c_rScore>=45 & c_rScore<=49)
(236 real changes made)

. replace c_pov_likelihood =7.2 if (c_rScore>=50 & c_rScore<=54)
(231 real changes made)

. replace c_pov_likelihood =4.3 if (c_rScore>=55 & c_rScore<=59)
(264 real changes made)

. replace c_pov_likelihood =2.2 if (c_rScore>=60 & c_rScore<=64)
(197 real changes made)

. replace c_pov_likelihood =1.1 if (c_rScore>=65 & c_rScore<=69)
(198 real changes made)

. replace c_pov_likelihood =0.8 if (c_rScore>=70 & c_rScore<=74)
(122 real changes made)

. replace c_pov_likelihood =0.3 if (c_rScore>=75 & c_rScore<=79)
(154 real changes made)

. replace c_pov_likelihood =0.0 if (c_rScore>=80 & c_rScore<=100)
(72 real changes made)

. 
. //merchants
. gen m_pov_likelihood = 91.4 if (m_rScore>=0 & m_rScore<=9)
(1,946 missing values generated)

. replace m_pov_likelihood =75.9 if (m_rScore>=10 & m_rScore<=14)
(4 real changes made)

. replace m_pov_likelihood =66.8 if (m_rScore>=15 & m_rScore<=19)
(0 real changes made)

. replace m_pov_likelihood =63.8 if (m_rScore>=20 & m_rScore<=24)
(0 real changes made)

. replace m_pov_likelihood =53.3 if (m_rScore>=25 & m_rScore<=29)
(37 real changes made)

. replace m_pov_likelihood =40.2 if (m_rScore>=30 & m_rScore<=34)
(29 real changes made)

. replace m_pov_likelihood =29.0 if (m_rScore>=35 & m_rScore<=39)
(103 real changes made)

. replace m_pov_likelihood =19.6 if (m_rScore>=40 & m_rScore<=44)
(66 real changes made)

. replace m_pov_likelihood =11.7 if (m_rScore>=45 & m_rScore<=49)
(125 real changes made)

. replace m_pov_likelihood =7.2 if (m_rScore>=50 & m_rScore<=54)
(133 real changes made)

. replace m_pov_likelihood =4.3 if (m_rScore>=55 & m_rScore<=59)
(201 real changes made)

. replace m_pov_likelihood =2.2 if (m_rScore>=60 & m_rScore<=64)
(206 real changes made)

. replace m_pov_likelihood =1.1 if (m_rScore>=65 & m_rScore<=69)
(349 real changes made)

. replace m_pov_likelihood =0.8 if (m_rScore>=70 & m_rScore<=74)
(242 real changes made)

. replace m_pov_likelihood =0.3 if (m_rScore>=75 & m_rScore<=79)
(248 real changes made)

. replace m_pov_likelihood =0.0 if (m_rScore>=80 & m_rScore<=100)
(203 real changes made)

. 
. 
. sum c_pov_likelihood m_pov_likelihood //13.9% vs 10.7% need weight? quasi-Ce
> nsus...

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |      2,054    13.85214    19.50827          0       91.4
m_pov_like~d |      2,054    10.70243    21.88483          0       91.4

. bys loccode: sum c_pov_likelihood m_pov_likelihood

------------------------------------------------------------------------------
-> loccode_sampling = 497200000022

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         12       5.225    5.860984          0       19.6
m_pov_like~d |         12           0           0          0          0

------------------------------------------------------------------------------
-> loccode_sampling = 497200000023

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         36    15.78056    15.04093         .3       63.8
m_pov_like~d |         36         4.2    2.526658        1.1        7.2

------------------------------------------------------------------------------
-> loccode_sampling = 497200000024

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         18    9.355556    10.96155          0       40.2
m_pov_like~d |         18        11.7           0       11.7       11.7

------------------------------------------------------------------------------
-> loccode_sampling = 497200000025

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         14    17.56429    25.24436         .3       91.4
m_pov_like~d |         14           0           0          0          0

------------------------------------------------------------------------------
-> loccode_sampling = 497200000026

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         23    8.143478    13.75437          0       53.3
m_pov_like~d |         23         2.2           0        2.2        2.2

------------------------------------------------------------------------------
-> loccode_sampling = 497200000027

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         24    26.14583    22.04322         .8       66.8
m_pov_like~d |         24       27.75    26.09952        2.2       53.3

------------------------------------------------------------------------------
-> loccode_sampling = 497200000028

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         14        21.9    33.71945         .3       91.4
m_pov_like~d |         14    .3071429    1.149223          0        4.3

------------------------------------------------------------------------------
-> loccode_sampling = 497200000029

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         23    9.269565    7.466558         .8         29
m_pov_like~d |         23         4.3           0        4.3        4.3

------------------------------------------------------------------------------
-> loccode_sampling = 497200000030

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         26       18.35    16.79193          0       63.8
m_pov_like~d |         26    4.876923    17.66099         .8       91.4

------------------------------------------------------------------------------
-> loccode_sampling = 497200000031

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         15    23.84667    35.86851         .3       91.4
m_pov_like~d |         15    .0933333    .2890049          0        1.1

------------------------------------------------------------------------------
-> loccode_sampling = 497200000032

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         31    13.15484    14.55321        1.1       63.8
m_pov_like~d |         31    3.690323    3.237628         .8        7.2

------------------------------------------------------------------------------
-> loccode_sampling = 497200000033

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         24     33.0875    39.54607         .8       91.4
m_pov_like~d |         24     10.0625     10.2661          0         29

------------------------------------------------------------------------------
-> loccode_sampling = 497200000034

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         17    18.24118    13.24283         .3       40.2
m_pov_like~d |         17    32.67059    15.13422         29       91.4

------------------------------------------------------------------------------
-> loccode_sampling = 498200000016

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         19    3.157895    4.208631          0       11.7
m_pov_like~d |         19    .5263158    .4747422          0        1.1

------------------------------------------------------------------------------
-> loccode_sampling = 498200000018

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         36    7.177778    20.84509          0       91.4
m_pov_like~d |         36    6.527778    15.65873         .3       91.4

------------------------------------------------------------------------------
-> loccode_sampling = 498200000021

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         13    12.09231      18.575         .3       66.8
m_pov_like~d |         13    .4846154     .321056          0         .8

------------------------------------------------------------------------------
-> loccode_sampling = 498200000022

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          8        6.15    9.517052         .3         29
m_pov_like~d |          8           0           0          0          0

------------------------------------------------------------------------------
-> loccode_sampling = 498200000025

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         24      3.3375    4.779195          0       19.6
m_pov_like~d |         24    .9958333    1.968553          0        7.2

------------------------------------------------------------------------------
-> loccode_sampling = 498200000026

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          6         9.6    15.61896          0       40.2
m_pov_like~d |          6         1.1           0        1.1        1.1

------------------------------------------------------------------------------
-> loccode_sampling = 498200144001

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         19    2.684211    2.808571          0       11.7
m_pov_like~d |         19         4.3           0        4.3        4.3

------------------------------------------------------------------------------
-> loccode_sampling = 498200176001

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         20        6.99    8.091314          0         29
m_pov_like~d |         20        40.2           0       40.2       40.2

------------------------------------------------------------------------------
-> loccode_sampling = 498200271001

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          7    10.07143    5.379193        4.3       19.6
m_pov_like~d |          7          .3           0         .3         .3

------------------------------------------------------------------------------
-> loccode_sampling = 499200000002

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          4      11.425    5.848291        7.2       19.6
m_pov_like~d |          4         4.3           0        4.3        4.3

------------------------------------------------------------------------------
-> loccode_sampling = 499200000003

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          6        5.35    5.103234          0       11.7
m_pov_like~d |          6    1.883333    .7756718         .3        2.2

------------------------------------------------------------------------------
-> loccode_sampling = 499200000010

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          7    4.914286    6.738306          0       19.6
m_pov_like~d |          7    .3142857    .5367451          0        1.1

------------------------------------------------------------------------------
-> loccode_sampling = 499200000011

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          3        24.5    18.36818        4.3       40.2
m_pov_like~d |          3         1.1           0        1.1        1.1

------------------------------------------------------------------------------
-> loccode_sampling = 499200000012

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         12         7.2    6.586074         .8       19.6
m_pov_like~d |         12         1.1           0        1.1        1.1

------------------------------------------------------------------------------
-> loccode_sampling = 499200000013

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         10       13.24    27.59119         .3       91.4
m_pov_like~d |         10       18.68     38.3282         .3       91.4

------------------------------------------------------------------------------
-> loccode_sampling = 499200000014

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          2        1.25    1.343503         .3        2.2
m_pov_like~d |          2         .15     .212132          0         .3

------------------------------------------------------------------------------
-> loccode_sampling = 499200000015

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          3    5.266667    1.674316        4.3        7.2
m_pov_like~d |          3    .7333333    .4041452         .3        1.1

------------------------------------------------------------------------------
-> loccode_sampling = 499200006001

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         15    13.15333    31.79009          0       91.4
m_pov_like~d |         15       18.84    37.55405         .3       91.4

------------------------------------------------------------------------------
-> loccode_sampling = 499200098001

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         15    25.01333    35.02895          0       91.4
m_pov_like~d |         15       73.12     37.8432          0       91.4

------------------------------------------------------------------------------
-> loccode_sampling = 499200100001

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          4        25.9    33.51537        4.3       75.9
m_pov_like~d |          4        75.9           0       75.9       75.9

------------------------------------------------------------------------------
-> loccode_sampling = 500200000001

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         10       16.44    28.03966          0       91.4
m_pov_like~d |         10       18.47    38.43951          0       91.4

------------------------------------------------------------------------------
-> loccode_sampling = 500200000002

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          6    15.28333    14.51791         .8       40.2
m_pov_like~d |          6        3.75    3.779286         .3        7.2

------------------------------------------------------------------------------
-> loccode_sampling = 500200000003

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         12       12.55    26.00914         .3       91.4
m_pov_like~d |         12        17.6    34.50673          0       91.4

------------------------------------------------------------------------------
-> loccode_sampling = 500200000004

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          8      10.425    12.08136         .3         29
m_pov_like~d |          8       6.575    1.767767        2.2        7.2

------------------------------------------------------------------------------
-> loccode_sampling = 500200000005

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         14    5.378572    6.438607          0       19.6
m_pov_like~d |         14    3.564286    6.843145          0       19.6

------------------------------------------------------------------------------
-> loccode_sampling = 500200000006

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          8      2.9375    6.770511          0       19.6
m_pov_like~d |          8         .25    .2672612          0         .8

------------------------------------------------------------------------------
-> loccode_sampling = 500200000007

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         18    12.03333    22.32047          0       91.4
m_pov_like~d |         18    4.133333     6.13917          0       19.6

------------------------------------------------------------------------------
-> loccode_sampling = 500200000008

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         14    7.492857    9.736329         .3         29
m_pov_like~d |         14    1.314286    3.039448          0       11.7

------------------------------------------------------------------------------
-> loccode_sampling = 500200000009

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         17    18.08824    17.31542         .3       63.8
m_pov_like~d |         17    27.09412    42.78678         .3       91.4

------------------------------------------------------------------------------
-> loccode_sampling = 500200000011

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         14    6.028571    4.353677          0       11.7
m_pov_like~d |         14    1.942857    2.870999          0        7.2

------------------------------------------------------------------------------
-> loccode_sampling = 500200000012

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         15        3.94    4.148459          0       11.7
m_pov_like~d |         15    .4066667    .4061433          0        1.1

------------------------------------------------------------------------------
-> loccode_sampling = 500200000013

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         24    6.570833    7.035777         .3         29
m_pov_like~d |         24    10.25833    19.26569        1.1       91.4

------------------------------------------------------------------------------
-> loccode_sampling = 500200000014

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          5        18.3    11.54816        2.2         29
m_pov_like~d |          5          .3           0         .3         .3

------------------------------------------------------------------------------
-> loccode_sampling = 500200000015

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         19    25.12105    22.56949          0       91.4
m_pov_like~d |         19    2.084211    .5047146          0        2.2

------------------------------------------------------------------------------
-> loccode_sampling = 500200000016

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          9    12.13333    4.976696        4.3       19.6
m_pov_like~d |          9    .3444445    .4503085          0        1.1

------------------------------------------------------------------------------
-> loccode_sampling = 500200000017

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         30    10.47333    12.14345          0       53.3
m_pov_like~d |         30         2.9    1.743955         .8        4.3

------------------------------------------------------------------------------
-> loccode_sampling = 500200000018

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         11    4.209091    6.248753         .3       19.6
m_pov_like~d |         11    5.990909    11.38055          0         29

------------------------------------------------------------------------------
-> loccode_sampling = 500200000019

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          5       20.06    14.99593        4.3       40.2
m_pov_like~d |          5        11.7           0       11.7       11.7

------------------------------------------------------------------------------
-> loccode_sampling = 500200000020

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         22        21.3    15.94034        1.1       53.3
m_pov_like~d |         22       15.65    4.042954       11.7       19.6

------------------------------------------------------------------------------
-> loccode_sampling = 500200000021

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         14    33.44286    20.16267        2.2       63.8
m_pov_like~d |         14        91.4           0       91.4       91.4

------------------------------------------------------------------------------
-> loccode_sampling = 500200000022

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         14    18.30714    18.24696        1.1       66.8
m_pov_like~d |         14         1.1    1.141524          0        2.2

------------------------------------------------------------------------------
-> loccode_sampling = 500200000023

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         17    13.65294    15.74417         .3       63.8
m_pov_like~d |         17        91.4           0       91.4       91.4

------------------------------------------------------------------------------
-> loccode_sampling = 500200000024

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         44    18.18182    11.81393         .8       40.2
m_pov_like~d |         44        .575    .5557836          0        1.1

------------------------------------------------------------------------------
-> loccode_sampling = 501200000002

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         18    .8444445    1.080426          0        4.3
m_pov_like~d |         18        1.25     .977542         .3        2.2

------------------------------------------------------------------------------
-> loccode_sampling = 501200000003

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         36    .9305556    1.449496          0        7.2
m_pov_like~d |         36    .5583333    .3245877         .3        1.1

------------------------------------------------------------------------------
-> loccode_sampling = 501200000004

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         47    1.676596    2.401196          0       11.7
m_pov_like~d |         47    .7489362    .3348409         .3        1.1

------------------------------------------------------------------------------
-> loccode_sampling = 501200000005

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         29    4.362069    4.361063         .3       19.6
m_pov_like~d |         29    3.627586    4.304557         .3       11.7

------------------------------------------------------------------------------
-> loccode_sampling = 501200000006

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         18    9.011111    9.340732         .8         29
m_pov_like~d |         18    2.166667    1.806524          0        4.3

------------------------------------------------------------------------------
-> loccode_sampling = 501200000007

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         18    .4111111    .3197221          0        1.1
m_pov_like~d |         18         .95    .1543487         .8        1.1

------------------------------------------------------------------------------
-> loccode_sampling = 501200000008

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         28    7.746429    6.204567         .8       19.6
m_pov_like~d |         28    .6428572    .4031621         .3        1.1

------------------------------------------------------------------------------
-> loccode_sampling = 502200000001

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         20       6.375    20.07318          0       91.4
m_pov_like~d |         20       1.505     1.37935          0        4.3

------------------------------------------------------------------------------
-> loccode_sampling = 502200000002

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         14    1.657143    1.332271         .3        4.3
m_pov_like~d |         14    1.557143    1.340903         .3        4.3

------------------------------------------------------------------------------
-> loccode_sampling = 502200000003

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         11    1.936364    1.374971         .3        4.3
m_pov_like~d |         11    2.809091    3.481222         .3        7.2

------------------------------------------------------------------------------
-> loccode_sampling = 502200000004

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         19    2.273684    2.491506         .3        7.2
m_pov_like~d |         19    2.078947    1.552982         .8        4.3

------------------------------------------------------------------------------
-> loccode_sampling = 502200000005

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          2        1.25    1.343503         .3        2.2
m_pov_like~d |          2         2.2           0        2.2        2.2

------------------------------------------------------------------------------
-> loccode_sampling = 502200000006

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         19    2.163158    2.829058         .3       11.7
m_pov_like~d |         19    2.573684    1.573334         .8        4.3

------------------------------------------------------------------------------
-> loccode_sampling = 502200000007

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          5        2.98    2.406657        1.1        7.2
m_pov_like~d |          5         1.1           0        1.1        1.1

------------------------------------------------------------------------------
-> loccode_sampling = 502200000008

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         36    10.03333    10.98235         .3       40.2
m_pov_like~d |         36    4.366667    5.269427         .3       11.7

------------------------------------------------------------------------------
-> loccode_sampling = 502200000010

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          6    3.533333    3.230892          0        7.2
m_pov_like~d |          6    2.533333    1.454189        1.1        4.3

------------------------------------------------------------------------------
-> loccode_sampling = 502200093001

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         12    2.741667    2.444087         .3        7.2
m_pov_like~d |         12         2.2           0        2.2        2.2

------------------------------------------------------------------------------
-> loccode_sampling = 503200000001

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          7    3.142857    3.137864          0        7.2
m_pov_like~d |          7    1.657143    .9271051         .3        2.2

------------------------------------------------------------------------------
-> loccode_sampling = 503200000002

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          6    2.783333    2.519854         .8        7.2
m_pov_like~d |          6         7.2           0        7.2        7.2

------------------------------------------------------------------------------
-> loccode_sampling = 503200000003

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          5        4.86    4.739515         .3       11.7
m_pov_like~d |          5        5.98    2.728003        1.1        7.2

------------------------------------------------------------------------------
-> loccode_sampling = 503200000004

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          9    18.35556    28.75357         .3       91.4
m_pov_like~d |          9    1.522222    2.546948          0        7.2

------------------------------------------------------------------------------
-> loccode_sampling = 503200000005

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          6    7.516667    9.477218          0       19.6
m_pov_like~d |          6         .15    .1643168          0         .3

------------------------------------------------------------------------------
-> loccode_sampling = 503200000006

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         10       13.49    21.31335          0       53.3
m_pov_like~d |         10        1.14    1.301452          0        4.3

------------------------------------------------------------------------------
-> loccode_sampling = 503200000007

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          6    8.616667    3.839488        2.2       11.7
m_pov_like~d |          6    .7166667    .3710346          0        1.1

------------------------------------------------------------------------------
-> loccode_sampling = 503200000008

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          4       1.425    1.972097          0        4.3
m_pov_like~d |          4          .3           0         .3         .3

------------------------------------------------------------------------------
-> loccode_sampling = 503200000009

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          3        16.3    14.63181         .3         29
m_pov_like~d |          3          .3           0         .3         .3

------------------------------------------------------------------------------
-> loccode_sampling = 504100000001

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         14    20.31429    14.92194         .8       40.2
m_pov_like~d |         14    25.01429    10.13151        1.1         29

------------------------------------------------------------------------------
-> loccode_sampling = 504100000002

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         15    6.273333    8.546968          0         29
m_pov_like~d |         15          .8           0         .8         .8

------------------------------------------------------------------------------
-> loccode_sampling = 504100000003

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          4        48.9     49.2652        1.1       91.4
m_pov_like~d |          4       1.725         .95         .3        2.2

------------------------------------------------------------------------------
-> loccode_sampling = 504100000004

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          4         3.5    2.958603         .3        7.2
m_pov_like~d |          4           4    3.695042         .8        7.2

------------------------------------------------------------------------------
-> loccode_sampling = 504100000005

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         14    2.364286     2.43614          0        7.2
m_pov_like~d |         14         3.7    3.510862        1.1       11.7

------------------------------------------------------------------------------
-> loccode_sampling = 504100000006

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          4        36.8    37.47888        7.2       91.4
m_pov_like~d |          4       7.075    8.408478        2.2       19.6

------------------------------------------------------------------------------
-> loccode_sampling = 504100000007

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         25      17.616    18.05888         .8       63.8
m_pov_like~d |         25        .908    .1469694         .8        1.1

------------------------------------------------------------------------------
-> loccode_sampling = 504100000008

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          3    8.066667    10.18643         .3       19.6
m_pov_like~d |          3          .3           0         .3         .3

------------------------------------------------------------------------------
-> loccode_sampling = 504100000009

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          4       68.55        45.7          0       91.4
m_pov_like~d |          4        .675         .25         .3         .8

------------------------------------------------------------------------------
-> loccode_sampling = 504100000010

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         15       22.98    18.73801        2.2       63.8
m_pov_like~d |         15        19.6           0       19.6       19.6

------------------------------------------------------------------------------
-> loccode_sampling = 504100000011

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          6    9.783333    8.751552         .3       19.6
m_pov_like~d |          6        20.6    36.47289         .8       91.4

------------------------------------------------------------------------------
-> loccode_sampling = 504100000012

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         13    8.192308    7.410405         .8       19.6
m_pov_like~d |         13         2.2           0        2.2        2.2

------------------------------------------------------------------------------
-> loccode_sampling = 504100000013

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         12    8.191667     10.4521        1.1       40.2
m_pov_like~d |         12         4.3           0        4.3        4.3

------------------------------------------------------------------------------
-> loccode_sampling = 504100000014

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          6         6.4    6.822023         .8       19.6
m_pov_like~d |          6    2.116667    1.806009         .3        4.3

------------------------------------------------------------------------------
-> loccode_sampling = 504100000015

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          7    3.771429    5.428233          0       11.7
m_pov_like~d |          7        19.6           0       19.6       19.6

------------------------------------------------------------------------------
-> loccode_sampling = 504100000016

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         20       5.645    5.779408         .8       19.6
m_pov_like~d |         20       4.095    3.521883         .3        7.2

------------------------------------------------------------------------------
-> loccode_sampling = 504100000017

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         12       31.25    21.04491        7.2       75.9
m_pov_like~d |         12        91.4           0       91.4       91.4

------------------------------------------------------------------------------
-> loccode_sampling = 504100000018

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          5        6.88     7.48111        1.1       19.6
m_pov_like~d |          5        3.04    1.150217        2.2        4.3

------------------------------------------------------------------------------
-> loccode_sampling = 504100000019

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         13    17.88462    15.40308        1.1       53.3
m_pov_like~d |         13          .3           0         .3         .3

------------------------------------------------------------------------------
-> loccode_sampling = 504100000020

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         13    13.96923    11.91458        1.1       40.2
m_pov_like~d |         13    49.73846    36.83344        7.2       91.4

------------------------------------------------------------------------------
-> loccode_sampling = 504100000021

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         24    13.79167    15.20003        1.1       63.8
m_pov_like~d |         24         2.2           0        2.2        2.2

------------------------------------------------------------------------------
-> loccode_sampling = 504100000022

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          2        6.95    6.717514        2.2       11.7
m_pov_like~d |          2         4.3           0        4.3        4.3

------------------------------------------------------------------------------
-> loccode_sampling = 504100000023

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          4      11.125     12.2587        1.1         29
m_pov_like~d |          4         1.1           0        1.1        1.1

------------------------------------------------------------------------------
-> loccode_sampling = 504100000024

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          3    3.233333    3.564173         .3        7.2
m_pov_like~d |          3          .3           0         .3         .3

------------------------------------------------------------------------------
-> loccode_sampling = 504100000025

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          4       3.225    3.163727         .3        7.2
m_pov_like~d |          4          .8           0         .8         .8

------------------------------------------------------------------------------
-> loccode_sampling = 504100031001

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          2        3.25    1.484924        2.2        4.3
m_pov_like~d |          2          .3           0         .3         .3

------------------------------------------------------------------------------
-> loccode_sampling = 504100104001

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         13    14.60769    16.80905          0       63.8
m_pov_like~d |         13        91.4           0       91.4       91.4

------------------------------------------------------------------------------
-> loccode_sampling = 507200000001

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         17    32.20588     14.9896        7.2       63.8
m_pov_like~d |         17          .3           0         .3         .3

------------------------------------------------------------------------------
-> loccode_sampling = 507200000002

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         26    31.23462    23.58389        4.3       91.4
m_pov_like~d |         26    46.48462    45.81988        1.1       91.4

------------------------------------------------------------------------------
-> loccode_sampling = 507200000003

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         24    24.29583    18.56639         .3       66.8
m_pov_like~d |         24        11.7           0       11.7       11.7

------------------------------------------------------------------------------
-> loccode_sampling = 507200000004

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         20      23.265    20.94547        1.1       53.3
m_pov_like~d |         20       2.725    1.786462         .8        4.3

------------------------------------------------------------------------------
-> loccode_sampling = 507200000005

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         13    20.64615    25.56533        4.3       91.4
m_pov_like~d |         13    6.707692    1.775041         .8        7.2

------------------------------------------------------------------------------
-> loccode_sampling = 507200000006

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         25      35.012    38.48381          0       91.4
m_pov_like~d |         25      10.172    18.56346          0       91.4

------------------------------------------------------------------------------
-> loccode_sampling = 507200000007

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         24      7.8875    7.407124          0         29
m_pov_like~d |         24        5.85    5.975821          0       11.7

------------------------------------------------------------------------------
-> loccode_sampling = 507200000008

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         11    35.99091    29.73034        7.2       91.4
m_pov_like~d |         11        50.1    20.80976        4.3       91.4

------------------------------------------------------------------------------
-> loccode_sampling = 507200000009

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          9        23.4    33.84058        1.1       91.4
m_pov_like~d |          9    6.688889    5.944418         .3       11.7

------------------------------------------------------------------------------
-> loccode_sampling = 507200000010

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         13    27.19231    25.33918        2.2       91.4
m_pov_like~d |         13    1.184615    .3050851        1.1        2.2

------------------------------------------------------------------------------
-> loccode_sampling = 507200000011

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         25      12.924    11.02116          0       40.2
m_pov_like~d |         25         4.3           0        4.3        4.3

------------------------------------------------------------------------------
-> loccode_sampling = 507200000012

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         20        33.1    22.06719        4.3       91.4
m_pov_like~d |         20       15.46    17.90264        7.2       91.4

------------------------------------------------------------------------------
-> loccode_sampling = 507200000013

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         25      18.768    15.91772        2.2       63.8
m_pov_like~d |         25       10.72    9.433186        1.1       19.6

------------------------------------------------------------------------------
-> loccode_sampling = 507200000014

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         17        19.9    21.07279         .3       63.8
m_pov_like~d |         17        53.3           0       53.3       53.3

------------------------------------------------------------------------------
-> loccode_sampling = 507200000015

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         15       24.02    18.89052          0       53.3
m_pov_like~d |         15          .8           0         .8         .8

------------------------------------------------------------------------------
-> loccode_sampling = 507200000016

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          8     27.3125    40.12119         .3       91.4
m_pov_like~d |          8      2.8625     1.98993         .3        4.3

------------------------------------------------------------------------------
-> loccode_sampling = 507200000017

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         23    19.29565    27.31502          0       91.4
m_pov_like~d |         23    26.52609    8.198238          0         29

------------------------------------------------------------------------------
-> loccode_sampling = 507200000018

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         37    17.11351    21.68904          0       75.9
m_pov_like~d |         37    1.748649    4.606796         .8         29

------------------------------------------------------------------------------
-> loccode_sampling = 507200000019

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         23     11.4913     11.9722          0       40.2
m_pov_like~d |         23          29           0         29         29

------------------------------------------------------------------------------
-> loccode_sampling = 507200000020

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         21    19.71429    21.14976          0       91.4
m_pov_like~d |         21    28.17619     3.77517       11.7         29

------------------------------------------------------------------------------
-> loccode_sampling = 507200000021

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         17    10.32353    14.58027          0       53.3
m_pov_like~d |         17    .4764706    .2462961         .3         .8

------------------------------------------------------------------------------
-> loccode_sampling = 507200000023

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         15    5.146667    3.742014         .8       11.7
m_pov_like~d |         15         7.2           0        7.2        7.2

------------------------------------------------------------------------------
-> loccode_sampling = 507200000024

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         20       19.32     16.6151          0       53.3
m_pov_like~d |         20        3.38    3.311479          0        7.2

------------------------------------------------------------------------------
-> loccode_sampling = 507200000025

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         15    10.36667    14.22581         .3       53.3
m_pov_like~d |         15         1.1           0        1.1        1.1

------------------------------------------------------------------------------
-> loccode_sampling = 507200000026

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         24    13.70833     13.2156         .8       40.2
m_pov_like~d |         24    4.458333    4.828891        1.1       11.7

------------------------------------------------------------------------------
-> loccode_sampling = 507200000027

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |          6    50.88333     44.6804        4.3       91.4
m_pov_like~d |          6        7.05    6.966707          0       19.6

------------------------------------------------------------------------------
-> loccode_sampling = 507200000028

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         25      17.848    19.09656         .3       63.8
m_pov_like~d |         25       1.012    .3045762          0        1.1

------------------------------------------------------------------------------
-> loccode_sampling = 507200000029

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~d |         21    26.10476    30.23211         .3       91.4
m_pov_like~d |         21    12.13333    16.38372         .3       40.2


. 
. 
. bys loccode: gen Nf=_N if mfemale==1
(1,283 missing values generated)

. bys loccode: gen Nm=_N if mfemale==0
(878 missing values generated)

. 
. 
. **income brackets: 1->2
. gen income_group= c1q7
(58 missing values generated)

. hist income_group
(bin=33, start=1, width=.12121212)

. bys loccode: egen vincome_group=mean(income_group) 

. bys loccode: egen vincome_groupf=mean(income_group) if mfemale==1
(1,286 missing values generated)

. bys loccode: egen vincome_groupm=mean(income_group) if mfemale==0
(886 missing values generated)

. 
. bys loccode: gen worse_incomeGp_FemaleV =(vincome_groupf < vincome_groupm)

. bys loccode: gen worse_incomeGp_FemaleV15 =(vincome_groupf < 1.5*vincome_gro
> upm) //to increase sample a bit, SEs later

. sum vincome_group vincome_groupf vincome_groupm worse_incomeGp_FemaleV worse
> _incomeGp_FemaleV15

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
vincome_gr~p |      2,054     1.37191    .5706161          1       3.75
vincome_gr~f |        768    1.479071    .7038304          1        4.2
vincome_gr~m |      1,168    1.309628    .4717222          1   3.777778
worse_inco~V |      2,054    .3739046    .4839566          0          1
worse_inc~15 |      2,054    .3739046    .4839566          0          1

. 
. 
. **indicator for loc where female-v-Poverty > male-v-Poverty
. bys loccode: egen vpov_rate=mean(m_pov_likelihood) 

. bys loccode: egen vpov_ratef=mean(m_pov_likelihood) if mfemale==1
(1,283 missing values generated)

. bys loccode: egen vpov_ratem=mean(m_pov_likelihood) if mfemale==0
(878 missing values generated)

. 
. bys loccode: gen worse_pov_FemaleV =(vpov_ratef > vpov_ratem)

. sum vpov_rate vpov_ratef vpov_ratem worse_pov_FemaleV

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
   vpov_rate |      2,054    10.70243    18.64348          0       91.4
  vpov_ratef |        771    5.966537    9.468614          0       53.3
  vpov_ratem |      1,176    6.464966    11.32043          0       75.9
worse_pov_~V |      2,054    .5725414    .4948302          0          1

. 
. 
. **baseline beliefs about misconduct?
. gen base_belief_overcharge = (c8q3==1)

. hist base_belief_overcharge
(bin=33, start=0, width=.03030303)

. sum base_belief_overcharge

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
base_belie~e |      2,054    .4230769    .4941677          0          1

. bys loccode: egen ocbase_belief_overcharge=mean(base_belief_overcharge) 

. bys loccode: egen fcbase_belief_overcharge=mean(base_belief_overcharge) if c
> female==1
(801 missing values generated)

. bys loccode: egen mcbase_belief_overcharge=mean(base_belief_overcharge) if c
> female==0
(1,311 missing values generated)

. 
. hist ocbase_belief_overcharge
(bin=33, start=0, width=.03030303)

. hist fcbase_belief_overcharge
(bin=30, start=0, width=.03333333)

. hist mcbase_belief_overcharge
(bin=27, start=0, width=.03703704)

. 
. 
. sum ocbase_belief_overcharge, d

                  ocbase_belief_overcharge
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs               2,054
25%           .2              0       Sum of wgt.       2,054

50%     .3888889                      Mean           .4230769
                        Largest       Std. dev.      .3181567
75%          .65              1
90%            1              1       Variance       .1012237
95%            1              1       Skewness       .4384695
99%            1              1       Kurtosis       2.094815

. bys loccode: gen under_bbelief = (ocbase_belief_overcharge < 0.388) //less t
> han overall median belief

. tab under_bbelief

under_bbeli |
         ef |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,038       50.54       50.54
          1 |      1,016       49.46      100.00
------------+-----------------------------------
      Total |      2,054      100.00

. 
. bys loccode: gen under_bbelief_fc = (fcbase_belief_overcharge < mcbase_belie
> f_overcharge)

. tab under_bbelief_fc

under_bbeli |
      ef_fc |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        801       39.00       39.00
          1 |      1,253       61.00      100.00
------------+-----------------------------------
      Total |      2,054      100.00

. 
. **Get unique vender (aka Mkt) ID?
. egen universalid = concat(loccode vendor_id)

. 
. // drop loccode vendor_id
. order ge01 ge02 ge03 ge04

. 
. saveold "$dta_loc_repl/01_intermediate/Mkt_fieldData_census", replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in
      older Stata formats)
file
    /Users/yazenkashlan/Library/CloudStorage/OneDrive-Personal/Documents/per
    > sonal/Berk/03_Work/Francis/Replication/data_final/01_intermediate/Mkt_
    > fieldData_census.dta saved

. 
. 
. 
. 
. 
. 
. 
end of do-file

. do "$do_loc/_basel-gender"                                      // generate 
> pct_female_Mktcensus[Star]

. /*
> Prepare data by gender
> 
> Input: 
>         - Mkt_fieldData_census
>         - sel_9Distr_137Local_List
> Output:
>         - pct_female_Mktcensus/Star
> 
> */
. 
. 
. **Mkt census: Get percent of femal vendors per locality; competition measure
> =hhi?
. use "$dta_loc_repl/01_intermediate/Mkt_fieldData_census", clear

. // gen double localityCode_j=loccode
. drop _merge

. // drop text_ge01 text_ge02
. merge m:1 ge02 using "$dta_loc_repl/00_Raw_anon/sel_9Distr_137Local_List"
(label _merge already defined)

    Result                      Number of obs
    -----------------------------------------
    Not matched                             0
    Matched                             2,054  (_merge==3)
    -----------------------------------------

. keep if _merge==3
(0 observations deleted)

. 
. **all vendors per locality =137 all here**
. bys ge01 ge02 ge03: keep if _n==1
(1,646 observations deleted)

. 
. **%of Female v's? Say, at least 3 vendors in locality
. bys ge02: egen pct_female = mean(mfemale)
(4 missing values generated)

. bys ge02: replace pct_female = pct_female*100
(316 real changes made)

. 
. bys ge02: gen sN =_N

. replace pct_female=. if sN <2
(38 real changes made, 38 to missing)

. twoway histogram pct_female, frac ytitle("Fraction of localities") xtitle("%
>  Female vendors per locality")

. gr export "$output_loc/baseline/pct_female_hist.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/baseline/pct_female_hist.eps saved as EPS format

. 
. 
. **Competition, hhi
. gen dailyTotMoney2=m2q4b //can use monthly sale: m1q8--correlates very well?
>  
(22 missing values generated)

. bys ge02: egen double sumdSales = sum(dailyTotMoney2)

. bys ge02: gen double shsqrd = (dailyTotMoney2/sumdSales)^2 
(25 missing values generated)

. bys ge02: egen double HHI=sum(shsqrd)

. *hist HHI //clean later? yes. drop missings etc..
. 
. // gen ge01 =districtName
. // gen ge02 =localityName 
. // gen ge03 =vn
. // gen double loccodee= loccode
. 
. keep pct_female HHI mfemale sN text_ge01 text_ge02 text_ge03 ge0* districtNa
> me localityName vn // loccode loccodee

. // keep pct_female HHI mfemale loccode sN
. *bys loccode: keep if _n==1
. 
. saveold "$dta_loc_repl/01_intermediate/pct_female_Mktcensus", replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in
      older Stata formats)
file
    /Users/yazenkashlan/Library/CloudStorage/OneDrive-Personal/Documents/per
    > sonal/Berk/03_Work/Francis/Replication/data_final/01_intermediate/pct_
    > female_Mktcensus.dta saved

. saveold "$dta_loc_repl/01_intermediate/pct_female_MktcensusStar", replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in
      older Stata formats)
file
    /Users/yazenkashlan/Library/CloudStorage/OneDrive-Personal/Documents/per
    > sonal/Berk/03_Work/Francis/Replication/data_final/01_intermediate/pct_
    > female_MktcensusStar.dta saved

. 
end of do-file

. do "$do_loc/_basel-repMkt"                                      // generate 
> repMkt_w[_xtics]

. /*
> Prepare market data
> 
> Input:
>         - Mkt_fieldData_census
> Outpu:
>         - repMkt 
>         - repMkt_w_xtics
> */
. 
. 
. use "$dta_loc_repl/01_intermediate/Mkt_fieldData_census", clear

. 
. **1: get representative Mkt (per locality)
. keep if (_merge==3) //only Merchant-Customer pairs that merged right? b/c ca
> n't study just 1
(133 observations deleted)

. drop _merge

. 
. **Get mkt summaries & restrictions?
. bys ge02: gen CustPerLocal= _N

. hist CustPerLocal // dis: tot no of cust per local
(bin=32, start=1, width=1.4375)

. sum CustPerLocal // 1 to 47 with avg=20.8<21 customers

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
CustPerLocal |      1,921    20.28579    10.04091          1         47

. 
. egen count_loccode=group(ge02)

. tab count_loccode, miss //137-> 134 (115?) success

group(ge02) |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         12        0.62        0.62
          2 |         36        1.87        2.50
          3 |         18        0.94        3.44
          4 |         13        0.68        4.11
          5 |         23        1.20        5.31
          6 |         24        1.25        6.56
          7 |         12        0.62        7.18
          8 |         23        1.20        8.38
          9 |         25        1.30        9.68
         10 |         12        0.62       10.31
         11 |         31        1.61       11.92
         12 |         17        0.88       12.81
         13 |         16        0.83       13.64
         14 |         19        0.99       14.63
         15 |         33        1.72       16.35
         16 |         13        0.68       17.02
         17 |          8        0.42       17.44
         18 |         24        1.25       18.69
         19 |          6        0.31       19.00
         20 |         19        0.99       19.99
         21 |         20        1.04       21.03
         22 |          7        0.36       21.40
         23 |          4        0.21       21.60
         24 |          6        0.31       21.92
         25 |          7        0.36       22.28
         26 |          3        0.16       22.44
         27 |         12        0.62       23.06
         28 |          8        0.42       23.48
         29 |          2        0.10       23.58
         30 |          3        0.16       23.74
         31 |         14        0.73       24.47
         32 |          4        0.21       24.67
         33 |          7        0.36       25.04
         34 |          6        0.31       25.35
         35 |         11        0.57       25.92
         36 |          8        0.42       26.34
         37 |         14        0.73       27.07
         38 |          8        0.42       27.49
         39 |         17        0.88       28.37
         40 |         14        0.73       29.10
         41 |         12        0.62       29.72
         42 |         14        0.73       30.45
         43 |         15        0.78       31.23
         44 |         23        1.20       32.43
         45 |          5        0.26       32.69
         46 |         18        0.94       33.63
         47 |          9        0.47       34.10
         48 |         30        1.56       35.66
         49 |         11        0.57       36.23
         50 |          5        0.26       36.49
         51 |         22        1.15       37.64
         52 |         14        0.73       38.37
         53 |         44        2.29       40.66
         54 |         18        0.94       41.59
         55 |         36        1.87       43.47
         56 |         47        2.45       45.91
         57 |         29        1.51       47.42
         58 |         18        0.94       48.36
         59 |         18        0.94       49.30
         60 |         28        1.46       50.75
         61 |         19        0.99       51.74
         62 |         14        0.73       52.47
         63 |         11        0.57       53.05
         64 |         19        0.99       54.03
         65 |          2        0.10       54.14
         66 |         19        0.99       55.13
         67 |          5        0.26       55.39
         68 |         36        1.87       57.26
         69 |          6        0.31       57.57
         70 |         12        0.62       58.20
         71 |          7        0.36       58.56
         72 |          6        0.31       58.88
         73 |          5        0.26       59.14
         74 |          8        0.42       59.55
         75 |          6        0.31       59.86
         76 |         10        0.52       60.39
         77 |          6        0.31       60.70
         78 |          4        0.21       60.91
         79 |          3        0.16       61.06
         80 |         14        0.73       61.79
         81 |         15        0.78       62.57
         82 |          3        0.16       62.73
         83 |          4        0.21       62.94
         84 |         14        0.73       63.66
         85 |          3        0.16       63.82
         86 |         25        1.30       65.12
         87 |          3        0.16       65.28
         88 |          1        0.05       65.33
         89 |         15        0.78       66.11
         90 |          5        0.26       66.37
         91 |         13        0.68       67.05
         92 |         12        0.62       67.67
         93 |          6        0.31       67.99
         94 |          7        0.36       68.35
         95 |         20        1.04       69.39
         96 |         12        0.62       70.02
         97 |          5        0.26       70.28
         98 |         13        0.68       70.95
         99 |          8        0.42       71.37
        100 |         24        1.25       72.62
        101 |          2        0.10       72.72
        102 |          4        0.21       72.93
        103 |          3        0.16       73.09
        104 |          4        0.21       73.30
        105 |          2        0.10       73.40
        106 |         13        0.68       74.08
        107 |         17        0.88       74.96
        108 |         12        0.62       75.59
        109 |         24        1.25       76.83
        110 |         20        1.04       77.88
        111 |         12        0.62       78.50
        112 |         17        0.88       79.39
        113 |         24        1.25       80.64
        114 |          8        0.42       81.05
        115 |          8        0.42       81.47
        116 |         12        0.62       82.09
        117 |         25        1.30       83.39
        118 |         18        0.94       84.33
        119 |         25        1.30       85.63
        120 |         17        0.88       86.52
        121 |         15        0.78       87.30
        122 |          6        0.31       87.61
        123 |         21        1.09       88.70
        124 |         37        1.93       90.63
        125 |         23        1.20       91.83
        126 |         20        1.04       92.87
        127 |         17        0.88       93.75
        128 |         15        0.78       94.53
        129 |         20        1.04       95.58
        130 |         15        0.78       96.36
        131 |         24        1.25       97.61
        132 |          3        0.16       97.76
        133 |         25        1.30       99.06
        134 |         18        0.94      100.00
------------+-----------------------------------
      Total |      1,921      100.00

. 
. egen local_by_vendor = group(ge02 ge03)

. tab local_by_vendor, miss //480-> 337 (315?) a drop

 group(ge02 |
      ge03) |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         12        0.62        0.62
          2 |         12        0.62        1.25
          3 |         12        0.62        1.87
          4 |         12        0.62        2.50
          5 |         18        0.94        3.44
          6 |         13        0.68        4.11
          7 |         23        1.20        5.31
          8 |         12        0.62        5.93
          9 |         12        0.62        6.56
         10 |         12        0.62        7.18
         11 |         23        1.20        8.38
         12 |         11        0.57        8.95
         13 |         14        0.73        9.68
         14 |         12        0.62       10.31
         15 |         17        0.88       11.19
         16 |         14        0.73       11.92
         17 |         12        0.62       12.55
         18 |          5        0.26       12.81
         19 |         16        0.83       13.64
         20 |          4        0.21       13.85
         21 |          8        0.42       14.26
         22 |          7        0.36       14.63
         23 |          6        0.31       14.94
         24 |          4        0.21       15.15
         25 |          5        0.26       15.41
         26 |          6        0.31       15.72
         27 |         12        0.62       16.35
         28 |          5        0.26       16.61
         29 |          6        0.31       16.92
         30 |          2        0.10       17.02
         31 |          8        0.42       17.44
         32 |          2        0.10       17.54
         33 |          5        0.26       17.80
         34 |          5        0.26       18.06
         35 |          9        0.47       18.53
         36 |          3        0.16       18.69
         37 |          6        0.31       19.00
         38 |         13        0.68       19.68
         39 |          6        0.31       19.99
         40 |         20        1.04       21.03
         41 |          7        0.36       21.40
         42 |          4        0.21       21.60
         43 |          5        0.26       21.86
         44 |          1        0.05       21.92
         45 |          5        0.26       22.18
         46 |          2        0.10       22.28
         47 |          3        0.16       22.44
         48 |         12        0.62       23.06
         49 |          1        0.05       23.11
         50 |          1        0.05       23.17
         51 |          1        0.05       23.22
         52 |          1        0.05       23.27
         53 |          2        0.10       23.37
         54 |          1        0.05       23.43
         55 |          1        0.05       23.48
         56 |          1        0.05       23.53
         57 |          1        0.05       23.58
         58 |          1        0.05       23.63
         59 |          1        0.05       23.69
         60 |          1        0.05       23.74
         61 |          2        0.10       23.84
         62 |          1        0.05       23.89
         63 |          1        0.05       23.95
         64 |          3        0.16       24.10
         65 |          3        0.16       24.26
         66 |          4        0.21       24.47
         67 |          4        0.21       24.67
         68 |          6        0.31       24.99
         69 |          1        0.05       25.04
         70 |          3        0.16       25.20
         71 |          3        0.16       25.35
         72 |          2        0.10       25.46
         73 |          1        0.05       25.51
         74 |          2        0.10       25.61
         75 |          2        0.10       25.72
         76 |          3        0.16       25.87
         77 |          1        0.05       25.92
         78 |          4        0.21       26.13
         79 |          1        0.05       26.18
         80 |          3        0.16       26.34
         81 |          2        0.10       26.44
         82 |          3        0.16       26.60
         83 |          2        0.10       26.70
         84 |          2        0.10       26.81
         85 |          3        0.16       26.97
         86 |          1        0.05       27.02
         87 |          1        0.05       27.07
         88 |          1        0.05       27.12
         89 |          3        0.16       27.28
         90 |          3        0.16       27.43
         91 |          1        0.05       27.49
         92 |          3        0.16       27.64
         93 |          2        0.10       27.75
         94 |          2        0.10       27.85
         95 |          2        0.10       27.95
         96 |          2        0.10       28.06
         97 |          1        0.05       28.11
         98 |          1        0.05       28.16
         99 |          2        0.10       28.27
        100 |          2        0.10       28.37
        101 |          3        0.16       28.53
        102 |          1        0.05       28.58
        103 |          2        0.10       28.68
        104 |          4        0.21       28.89
        105 |          3        0.16       29.05
        106 |          1        0.05       29.10
        107 |         12        0.62       29.72
        108 |          4        0.21       29.93
        109 |          7        0.36       30.30
        110 |          3        0.16       30.45
        111 |          5        0.26       30.71
        112 |          2        0.10       30.82
        113 |          1        0.05       30.87
        114 |          2        0.10       30.97
        115 |          5        0.26       31.23
        116 |          5        0.26       31.49
        117 |          7        0.36       31.86
        118 |         11        0.57       32.43
        119 |          5        0.26       32.69
        120 |         18        0.94       33.63
        121 |          2        0.10       33.73
        122 |          3        0.16       33.89
        123 |          4        0.21       34.10
        124 |         12        0.62       34.72
        125 |         18        0.94       35.66
        126 |          2        0.10       35.76
        127 |          1        0.05       35.81
        128 |          2        0.10       35.92
        129 |          2        0.10       36.02
        130 |          1        0.05       36.07
        131 |          3        0.16       36.23
        132 |          2        0.10       36.34
        133 |          3        0.16       36.49
        134 |         11        0.57       37.06
        135 |         11        0.57       37.64
        136 |          7        0.36       38.00
        137 |          7        0.36       38.37
        138 |         21        1.09       39.46
        139 |         23        1.20       40.66
        140 |          3        0.16       40.81
        141 |          6        0.31       41.12
        142 |          6        0.31       41.44
        143 |          3        0.16       41.59
        144 |          6        0.31       41.91
        145 |          6        0.31       42.22
        146 |          6        0.31       42.53
        147 |          3        0.16       42.69
        148 |          6        0.31       43.00
        149 |          9        0.47       43.47
        150 |          4        0.21       43.68
        151 |          5        0.26       43.94
        152 |          6        0.31       44.25
        153 |          9        0.47       44.72
        154 |          7        0.36       45.08
        155 |         10        0.52       45.60
        156 |          6        0.31       45.91
        157 |          6        0.31       46.23
        158 |          6        0.31       46.54
        159 |          6        0.31       46.85
        160 |          3        0.16       47.01
        161 |          2        0.10       47.11
        162 |          3        0.16       47.27
        163 |          3        0.16       47.42
        164 |          6        0.31       47.74
        165 |          6        0.31       48.05
        166 |          6        0.31       48.36
        167 |          9        0.47       48.83
        168 |          9        0.47       49.30
        169 |          6        0.31       49.61
        170 |         12        0.62       50.23
        171 |         10        0.52       50.75
        172 |          1        0.05       50.81
        173 |          2        0.10       50.91
        174 |          4        0.21       51.12
        175 |          2        0.10       51.22
        176 |          1        0.05       51.28
        177 |          5        0.26       51.54
        178 |          2        0.10       51.64
        179 |          2        0.10       51.74
        180 |          1        0.05       51.80
        181 |          2        0.10       51.90
        182 |          1        0.05       51.95
        183 |          1        0.05       52.00
        184 |          2        0.10       52.11
        185 |          1        0.05       52.16
        186 |          2        0.10       52.26
        187 |          3        0.16       52.42
        188 |          1        0.05       52.47
        189 |          4        0.21       52.68
        190 |          4        0.21       52.89
        191 |          3        0.16       53.05
        192 |          2        0.10       53.15
        193 |          6        0.31       53.46
        194 |          2        0.10       53.57
        195 |          3        0.16       53.72
        196 |          6        0.31       54.03
        197 |          2        0.10       54.14
        198 |          2        0.10       54.24
        199 |          3        0.16       54.40
        200 |          1        0.05       54.45
        201 |          1        0.05       54.50
        202 |          3        0.16       54.66
        203 |          4        0.21       54.87
        204 |          3        0.16       55.02
        205 |          2        0.10       55.13
        206 |          5        0.26       55.39
        207 |         12        0.62       56.01
        208 |         12        0.62       56.64
        209 |         12        0.62       57.26
        210 |          2        0.10       57.37
        211 |          2        0.10       57.47
        212 |          2        0.10       57.57
        213 |         12        0.62       58.20
        214 |          2        0.10       58.30
        215 |          2        0.10       58.41
        216 |          2        0.10       58.51
        217 |          1        0.05       58.56
        218 |          6        0.31       58.88
        219 |          4        0.21       59.08
        220 |          1        0.05       59.14
        221 |          1        0.05       59.19
        222 |          1        0.05       59.24
        223 |          1        0.05       59.29
        224 |          2        0.10       59.40
        225 |          1        0.05       59.45
        226 |          2        0.10       59.55
        227 |          3        0.16       59.71
        228 |          3        0.16       59.86
        229 |          1        0.05       59.92
        230 |          3        0.16       60.07
        231 |          2        0.10       60.18
        232 |          1        0.05       60.23
        233 |          1        0.05       60.28
        234 |          1        0.05       60.33
        235 |          1        0.05       60.39
        236 |          1        0.05       60.44
        237 |          4        0.21       60.65
        238 |          1        0.05       60.70
        239 |          4        0.21       60.91
        240 |          3        0.16       61.06
        241 |         12        0.62       61.69
        242 |          2        0.10       61.79
        243 |         15        0.78       62.57
        244 |          3        0.16       62.73
        245 |          2        0.10       62.83
        246 |          2        0.10       62.94
        247 |          2        0.10       63.04
        248 |          2        0.10       63.14
        249 |          2        0.10       63.25
        250 |          2        0.10       63.35
        251 |          6        0.31       63.66
        252 |          2        0.10       63.77
        253 |          1        0.05       63.82
        254 |         16        0.83       64.65
        255 |          9        0.47       65.12
        256 |          3        0.16       65.28
        257 |          1        0.05       65.33
        258 |         15        0.78       66.11
        259 |          4        0.21       66.32
        260 |          1        0.05       66.37
        261 |         13        0.68       67.05
        262 |         12        0.62       67.67
        263 |          2        0.10       67.78
        264 |          1        0.05       67.83
        265 |          2        0.10       67.93
        266 |          1        0.05       67.99
        267 |          7        0.36       68.35
        268 |         11        0.57       68.92
        269 |          9        0.47       69.39
        270 |         12        0.62       70.02
        271 |          3        0.16       70.17
        272 |          2        0.10       70.28
        273 |         13        0.68       70.95
        274 |          4        0.21       71.16
        275 |          4        0.21       71.37
        276 |         13        0.68       72.05
        277 |         11        0.57       72.62
        278 |          2        0.10       72.72
        279 |          4        0.21       72.93
        280 |          3        0.16       73.09
        281 |          4        0.21       73.30
        282 |          2        0.10       73.40
        283 |         13        0.68       74.08
        284 |         17        0.88       74.96
        285 |         12        0.62       75.59
        286 |          9        0.47       76.05
        287 |         15        0.78       76.83
        288 |         11        0.57       77.41
        289 |          9        0.47       77.88
        290 |         12        0.62       78.50
        291 |          6        0.31       78.81
        292 |          2        0.10       78.92
        293 |          4        0.21       79.13
        294 |          3        0.16       79.28
        295 |          2        0.10       79.39
        296 |         12        0.62       80.01
        297 |         12        0.62       80.64
        298 |          8        0.42       81.05
        299 |          5        0.26       81.31
        300 |          3        0.16       81.47
        301 |         12        0.62       82.09
        302 |         25        1.30       83.39
        303 |         18        0.94       84.33
        304 |         13        0.68       85.01
        305 |         12        0.62       85.63
        306 |         17        0.88       86.52
        307 |         15        0.78       87.30
        308 |          4        0.21       87.51
        309 |          1        0.05       87.56
        310 |          1        0.05       87.61
        311 |         21        1.09       88.70
        312 |          8        0.42       89.12
        313 |         15        0.78       89.90
        314 |         13        0.68       90.58
        315 |          1        0.05       90.63
        316 |         23        1.20       91.83
        317 |         20        1.04       92.87
        318 |          5        0.26       93.13
        319 |          6        0.31       93.44
        320 |          6        0.31       93.75
        321 |         15        0.78       94.53
        322 |          9        0.47       95.00
        323 |          7        0.36       95.37
        324 |          4        0.21       95.58
        325 |         15        0.78       96.36
        326 |         15        0.78       97.14
        327 |          2        0.10       97.24
        328 |          7        0.36       97.61
        329 |          3        0.16       97.76
        330 |          9        0.47       98.23
        331 |          2        0.10       98.33
        332 |         14        0.73       99.06
        333 |          4        0.21       99.27
        334 |          3        0.16       99.43
        335 |          4        0.21       99.64
        336 |          5        0.26       99.90
        337 |          2        0.10      100.00
------------+-----------------------------------
      Total |      1,921      100.00

. tab Mkt, miss //480-> 337 (315?) a drop

group(locco |
de_sampling |
 vendor_id) |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         12        0.62        0.62
          2 |         12        0.62        1.25
          3 |         12        0.62        1.87
          4 |         12        0.62        2.50
          5 |         18        0.94        3.44
          6 |         13        0.68        4.11
          8 |         23        1.20        5.31
          9 |         12        0.62        5.93
         10 |         12        0.62        6.56
         11 |         12        0.62        7.18
         14 |         23        1.20        8.38
         15 |         11        0.57        8.95
         16 |         14        0.73        9.68
         18 |         12        0.62       10.31
         22 |         17        0.88       11.19
         23 |         14        0.73       11.92
         24 |         12        0.62       12.55
         29 |          5        0.26       12.81
         33 |         16        0.83       13.64
         35 |          4        0.21       13.85
         36 |          8        0.42       14.26
         37 |          7        0.36       14.63
         39 |          6        0.31       14.94
         40 |          4        0.21       15.15
         41 |          5        0.26       15.41
         42 |          6        0.31       15.72
         43 |         12        0.62       16.35
         46 |          5        0.26       16.61
         47 |          6        0.31       16.92
         48 |          2        0.10       17.02
         49 |          8        0.42       17.44
         50 |          2        0.10       17.54
         51 |          5        0.26       17.80
         52 |          5        0.26       18.06
         53 |          9        0.47       18.53
         54 |          3        0.16       18.69
         55 |          6        0.31       19.00
         56 |         13        0.68       19.68
         57 |          6        0.31       19.99
         58 |         20        1.04       21.03
         59 |          7        0.36       21.40
         60 |          4        0.21       21.60
         61 |          5        0.26       21.86
         62 |          1        0.05       21.92
         63 |          5        0.26       22.18
         64 |          2        0.10       22.28
         65 |          3        0.16       22.44
         66 |         12        0.62       23.06
         68 |          1        0.05       23.11
         69 |          1        0.05       23.17
         70 |          1        0.05       23.22
         71 |          1        0.05       23.27
         72 |          2        0.10       23.37
         73 |          1        0.05       23.43
         74 |          1        0.05       23.48
         76 |          1        0.05       23.53
         77 |          1        0.05       23.58
         78 |          1        0.05       23.63
         79 |          1        0.05       23.69
         80 |          1        0.05       23.74
         81 |          2        0.10       23.84
         82 |          1        0.05       23.89
         83 |          1        0.05       23.95
         84 |          3        0.16       24.10
         85 |          3        0.16       24.26
         86 |          4        0.21       24.47
         91 |          4        0.21       24.67
         92 |          6        0.31       24.99
         93 |          1        0.05       25.04
         96 |          3        0.16       25.20
         97 |          3        0.16       25.35
         98 |          2        0.10       25.46
         99 |          1        0.05       25.51
        100 |          2        0.10       25.61
        101 |          2        0.10       25.72
        102 |          3        0.16       25.87
        104 |          1        0.05       25.92
        105 |          4        0.21       26.13
        106 |          1        0.05       26.18
        107 |          3        0.16       26.34
        108 |          2        0.10       26.44
        109 |          3        0.16       26.60
        110 |          2        0.10       26.70
        111 |          2        0.10       26.81
        112 |          3        0.16       26.97
        113 |          1        0.05       27.02
        114 |          1        0.05       27.07
        115 |          1        0.05       27.12
        116 |          3        0.16       27.28
        117 |          3        0.16       27.43
        118 |          1        0.05       27.49
        119 |          3        0.16       27.64
        120 |          2        0.10       27.75
        121 |          2        0.10       27.85
        122 |          2        0.10       27.95
        123 |          2        0.10       28.06
        125 |          1        0.05       28.11
        126 |          1        0.05       28.16
        127 |          2        0.10       28.27
        128 |          2        0.10       28.37
        129 |          3        0.16       28.53
        130 |          1        0.05       28.58
        131 |          2        0.10       28.68
        132 |          4        0.21       28.89
        133 |          3        0.16       29.05
        134 |          1        0.05       29.10
        136 |         12        0.62       29.72
        137 |          4        0.21       29.93
        138 |          7        0.36       30.30
        139 |          3        0.16       30.45
        140 |          5        0.26       30.71
        141 |          2        0.10       30.82
        142 |          1        0.05       30.87
        143 |          2        0.10       30.97
        144 |          5        0.26       31.23
        146 |          5        0.26       31.49
        147 |          7        0.36       31.86
        148 |         11        0.57       32.43
        149 |          5        0.26       32.69
        150 |         18        0.94       33.63
        152 |          2        0.10       33.73
        153 |          3        0.16       33.89
        154 |          4        0.21       34.10
        155 |         12        0.62       34.72
        156 |         18        0.94       35.66
        157 |          2        0.10       35.76
        158 |          1        0.05       35.81
        159 |          2        0.10       35.92
        160 |          2        0.10       36.02
        161 |          1        0.05       36.07
        162 |          3        0.16       36.23
        163 |          2        0.10       36.34
        164 |          3        0.16       36.49
        165 |         11        0.57       37.06
        166 |         11        0.57       37.64
        168 |          7        0.36       38.00
        169 |          7        0.36       38.37
        171 |         21        1.09       39.46
        172 |         23        1.20       40.66
        173 |          3        0.16       40.81
        174 |          6        0.31       41.12
        175 |          6        0.31       41.44
        176 |          3        0.16       41.59
        177 |          6        0.31       41.91
        178 |          6        0.31       42.22
        179 |          6        0.31       42.53
        180 |          3        0.16       42.69
        181 |          6        0.31       43.00
        182 |          9        0.47       43.47
        183 |          4        0.21       43.68
        184 |          5        0.26       43.94
        185 |          6        0.31       44.25
        186 |          9        0.47       44.72
        187 |          7        0.36       45.08
        188 |         10        0.52       45.60
        189 |          6        0.31       45.91
        190 |          6        0.31       46.23
        191 |          6        0.31       46.54
        192 |          6        0.31       46.85
        193 |          3        0.16       47.01
        194 |          2        0.10       47.11
        195 |          3        0.16       47.27
        196 |          3        0.16       47.42
        197 |          6        0.31       47.74
        198 |          6        0.31       48.05
        199 |          6        0.31       48.36
        200 |          9        0.47       48.83
        201 |          9        0.47       49.30
        202 |          6        0.31       49.61
        203 |         12        0.62       50.23
        204 |         10        0.52       50.75
        205 |          1        0.05       50.81
        206 |          2        0.10       50.91
        207 |          4        0.21       51.12
        208 |          2        0.10       51.22
        209 |          1        0.05       51.28
        210 |          5        0.26       51.54
        211 |          2        0.10       51.64
        213 |          2        0.10       51.74
        214 |          1        0.05       51.80
        215 |          2        0.10       51.90
        216 |          1        0.05       51.95
        217 |          1        0.05       52.00
        218 |          2        0.10       52.11
        219 |          1        0.05       52.16
        220 |          2        0.10       52.26
        221 |          3        0.16       52.42
        222 |          1        0.05       52.47
        223 |          4        0.21       52.68
        224 |          4        0.21       52.89
        225 |          3        0.16       53.05
        226 |          2        0.10       53.15
        227 |          6        0.31       53.46
        228 |          2        0.10       53.57
        229 |          3        0.16       53.72
        230 |          6        0.31       54.03
        231 |          2        0.10       54.14
        232 |          2        0.10       54.24
        233 |          3        0.16       54.40
        234 |          1        0.05       54.45
        235 |          1        0.05       54.50
        236 |          3        0.16       54.66
        237 |          4        0.21       54.87
        238 |          3        0.16       55.02
        239 |          2        0.10       55.13
        240 |          5        0.26       55.39
        241 |         12        0.62       56.01
        242 |         12        0.62       56.64
        243 |         12        0.62       57.26
        244 |          2        0.10       57.37
        245 |          2        0.10       57.47
        246 |          2        0.10       57.57
        247 |         12        0.62       58.20
        248 |          2        0.10       58.30
        249 |          2        0.10       58.41
        250 |          2        0.10       58.51
        251 |          1        0.05       58.56
        252 |          6        0.31       58.88
        253 |          4        0.21       59.08
        254 |          1        0.05       59.14
        255 |          1        0.05       59.19
        256 |          1        0.05       59.24
        257 |          1        0.05       59.29
        259 |          2        0.10       59.40
        260 |          1        0.05       59.45
        261 |          2        0.10       59.55
        262 |          3        0.16       59.71
        263 |          3        0.16       59.86
        264 |          1        0.05       59.92
        265 |          3        0.16       60.07
        266 |          2        0.10       60.18
        267 |          1        0.05       60.23
        268 |          1        0.05       60.28
        269 |          1        0.05       60.33
        270 |          1        0.05       60.39
        271 |          1        0.05       60.44
        272 |          4        0.21       60.65
        273 |          1        0.05       60.70
        274 |          4        0.21       60.91
        275 |          3        0.16       61.06
        276 |         12        0.62       61.69
        277 |          2        0.10       61.79
        278 |         15        0.78       62.57
        279 |          3        0.16       62.73
        281 |          2        0.10       62.83
        282 |          2        0.10       62.94
        283 |          2        0.10       63.04
        284 |          2        0.10       63.14
        285 |          2        0.10       63.25
        286 |          2        0.10       63.35
        287 |          6        0.31       63.66
        288 |          2        0.10       63.77
        289 |          1        0.05       63.82
        291 |         16        0.83       64.65
        292 |          9        0.47       65.12
        293 |          3        0.16       65.28
        294 |          1        0.05       65.33
        298 |         15        0.78       66.11
        299 |          4        0.21       66.32
        300 |          1        0.05       66.37
        302 |         13        0.68       67.05
        303 |         12        0.62       67.67
        304 |          2        0.10       67.78
        305 |          1        0.05       67.83
        306 |          2        0.10       67.93
        307 |          1        0.05       67.99
        308 |          7        0.36       68.35
        309 |         11        0.57       68.92
        310 |          9        0.47       69.39
        311 |         12        0.62       70.02
        312 |          3        0.16       70.17
        313 |          2        0.10       70.28
        314 |         13        0.68       70.95
        315 |          4        0.21       71.16
        317 |          4        0.21       71.37
        318 |         13        0.68       72.05
        319 |         11        0.57       72.62
        320 |          2        0.10       72.72
        321 |          4        0.21       72.93
        322 |          3        0.16       73.09
        323 |          4        0.21       73.30
        324 |          2        0.10       73.40
        325 |         13        0.68       74.08
        326 |         17        0.88       74.96
        327 |         12        0.62       75.59
        330 |          9        0.47       76.05
        331 |         15        0.78       76.83
        332 |         11        0.57       77.41
        333 |          9        0.47       77.88
        334 |         12        0.62       78.50
        336 |          6        0.31       78.81
        339 |          2        0.10       78.92
        341 |          4        0.21       79.13
        343 |          3        0.16       79.28
        346 |          2        0.10       79.39
        349 |         12        0.62       80.01
        350 |         12        0.62       80.64
        351 |          8        0.42       81.05
        355 |          5        0.26       81.31
        356 |          3        0.16       81.47
        358 |         12        0.62       82.09
        360 |         25        1.30       83.39
        362 |         18        0.94       84.33
        364 |         13        0.68       85.01
        365 |         12        0.62       85.63
        366 |         17        0.88       86.52
        367 |         15        0.78       87.30
        368 |          4        0.21       87.51
        369 |          1        0.05       87.56
        372 |          1        0.05       87.61
        373 |         21        1.09       88.70
        376 |          8        0.42       89.12
        377 |         15        0.78       89.90
        378 |         13        0.68       90.58
        379 |          1        0.05       90.63
        380 |         23        1.20       91.83
        381 |         20        1.04       92.87
        383 |          5        0.26       93.13
        384 |          6        0.31       93.44
        385 |          6        0.31       93.75
        386 |         15        0.78       94.53
        387 |          9        0.47       95.00
        388 |          7        0.36       95.37
        389 |          4        0.21       95.58
        390 |         15        0.78       96.36
        391 |         15        0.78       97.14
        392 |          2        0.10       97.24
        393 |          7        0.36       97.61
        394 |          3        0.16       97.76
        398 |          9        0.47       98.23
        399 |          2        0.10       98.33
        400 |         14        0.73       99.06
        401 |          4        0.21       99.27
        402 |          3        0.16       99.43
        403 |          4        0.21       99.64
        405 |          5        0.26       99.90
        407 |          2        0.10      100.00
------------+-----------------------------------
      Total |      1,921      100.00

. 
. // bys ge02: gen mktFip = group(ge03)
. 
. hist MktPerLocal //dis: tot no of Mkt(/merch) per local
(bin=32, start=1, width=.34375)

. sum MktPerLocal // 1 to 12 with avg=3.2<4 merchants

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
 MktPerLocal |      1,921    3.219677    2.395884          1         12

. tab MktPerLocal

MktPerLocal |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        450       23.43       23.43
          2 |        570       29.67       53.10
          3 |        375       19.52       72.62
          4 |         92        4.79       77.41
          5 |         78        4.06       81.47
          6 |         61        3.18       84.64
          7 |        166        8.64       93.28
          8 |         45        2.34       95.63
          9 |         50        2.60       98.23
         10 |         17        0.88       99.12
         12 |         17        0.88      100.00
------------+-----------------------------------
      Total |      1,921      100.00

. 
. bys ge02 ge03: gen CustPer_w_Mkt = _N

. hist CustPer_w_Mkt //dis: tot no of within-Cust per mkt
(bin=32, start=1, width=.75)

. tab CustPer_w_Mkt

CustPer_w_M |
         kt |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         59        3.07        3.07
          2 |        130        6.77        9.84
          3 |        129        6.72       16.55
          4 |        108        5.62       22.18
          5 |         85        4.42       26.60
          6 |        168        8.75       35.35
          7 |         70        3.64       38.99
          8 |         32        1.67       40.66
          9 |         99        5.15       45.81
         10 |         20        1.04       46.85
         11 |         77        4.01       50.86
         12 |        324       16.87       67.73
         13 |        104        5.41       73.14
         14 |         42        2.19       75.33
         15 |        120        6.25       81.57
         16 |         32        1.67       83.24
         17 |         51        2.65       85.89
         18 |         72        3.75       89.64
         20 |         40        2.08       91.72
         21 |         42        2.19       93.91
         23 |         92        4.79       98.70
         25 |         25        1.30      100.00
------------+-----------------------------------
      Total |      1,921      100.00

. 
. **get "rep market" per each locality?
. preserve 

.         bys loccode_sampling vendor_id: keep if _n==1
(1,584 observations deleted)

.         set seed 12345

.         bys loccode_sampling: gen rand_num = uniform()

.         bys loccode_sampling: gen x = _N

.         by loccode_sampling (rand_num), sort: gen sample_repMkt = _n==x

.         tab sample_repMkt, miss

sample_repM |
         kt |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        203       60.24       60.24
          1 |        134       39.76      100.00
------------+-----------------------------------
      Total |        337      100.00

. 
.         *gen rand_num = uniform()
.         *by loccode (rand_num), sort: gen sample_repMkt = _n==1
.         *tab sample_repMkt, miss
. 
.         keep ln loccode_sampling ge02 ge03 vn Mkt rand_num sample_repMkt* m1
> q9a m1q9b m1q0d worse_pov_FemaleV worse_incomeGp_FemaleV worse_incomeGp_Fema
> leV15 base_belief_overcharge ocbase_belief_overcharge fcbase_belief_overchar
> ge mcbase_belief_overcharge under_bbelief under_bbelief_fc market_to_drop

.         *keep ln loccode vendor_id vn cn Mkt rand_num sample_repMkt m1q9a c1
> q8a m1q9b c1q8b m1q0d c1q0b
. 
.         **more cleaning? 3 more drops...no info
.         drop if market_to_drop == 1
(5 observations deleted)

.         tab sample_repMkt, miss //130 loc or repMkts now...

sample_repM |
         kt |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        202       60.84       60.84
          1 |        130       39.16      100.00
------------+-----------------------------------
      Total |        332      100.00

.                 
.         save "$dta_loc_repl/01_intermediate/repMkt", replace
file
    /Users/yazenkashlan/Library/CloudStorage/OneDrive-Personal/Documents/per
    > sonal/Berk/03_Work/Francis/Replication/data_final/01_intermediate/repM
    > kt.dta saved

. restore

. 
. **QUEST: ***balance achieved -- DIFF from population?
. merge m:1 ge02 ge03 using "$dta_loc_repl/01_intermediate/repMkt.dta"

    Result                      Number of obs
    -----------------------------------------
    Not matched                            65
        from master                        65  (_merge==1)
        from using                          0  (_merge==2)

    Matched                             1,856  (_merge==3)
    -----------------------------------------

. 
. ** save
. save "$dta_loc_repl/01_intermediate/repMkt_w_xtics", replace
file
    /Users/yazenkashlan/Library/CloudStorage/OneDrive-Personal/Documents/per
    > sonal/Berk/03_Work/Francis/Replication/data_final/01_intermediate/repM
    > kt_w_xtics.dta saved

. 
. 
. ** -------------------------------------------------------------------------
> ----
. ** Source: Commands_Test_f.do
. 
. 
. *3b mkt, transactions?
. gen distToBank= c3q3a 
(1,486 missing values generated)

. gen walkTimeBank= c3q3b 
(1,486 missing values generated)

. gen bankUser = (c3q4==1)

. replace bankUser=. if missing(c3q4)
(1,486 real changes made, 1,486 to missing)

. 
. gen distTopostOffice = c3q7a
(1,802 missing values generated)

. gen walkTimepostOffice = c3q7b
(1,802 missing values generated)

. gen postOffUser=(c3q8==1)

. replace postOffUser=. if missing(c3q8)
(1,802 real changes made, 1,802 to missing)

. 
. gen distToMMoney= c4q2a
(154 missing values generated)

. gen walkTimeMMoney= c4q2b
(154 missing values generated)

. gen MMoneyUser=(c4q3==1)

. replace MMoneyUser=. if missing(c4q3)
(154 real changes made, 154 to missing)

. 
. *3c borrow + save behavior?
. gen likelyborrowMMoney =c5q1
(3 missing values generated)

. gen likelysaveMMoney =c5q5
(3 missing values generated)

. 
. 
. *keep if sample_repMkt==1
. keep if _merge==3
(65 observations deleted)

. drop _merge

. save "$dta_loc_repl/01_intermediate/Mkt_fieldData_sample_repMkt", replace
file
    /Users/yazenkashlan/Library/CloudStorage/OneDrive-Personal/Documents/per
    > sonal/Berk/03_Work/Francis/Replication/data_final/01_intermediate/Mkt_
    > fieldData_sample_repMkt.dta saved

. 
. 
end of do-file

. 
. ** treatment assigmnent
. do "$do_loc/_basel-interventions1"              // generate ONLY_4TrtGroups_
> 9dist

. /*
> Interventions at the market-level (generated here)
> 
> Input:
>         - repMkt
>         - sel_9Distr_137Local_List
> Output:
>         - ONLY_4TrtGroups_9dist
> */
. 
. 
. **ONLY: dta for field-Auditors: the 130 repMkts?
. use "$dta_loc_repl/01_intermediate/repMkt", clear

. tab sample_repMkt, miss

sample_repM |
         kt |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        202       60.84       60.84
          1 |        130       39.16      100.00
------------+-----------------------------------
      Total |        332      100.00

. keep if sample_repMkt==1
(202 observations deleted)

. 
. merge 1:1 ge02 using "$dta_loc_repl/00_raw_anon/sel_9Distr_137Local_List"

    Result                      Number of obs
    -----------------------------------------
    Not matched                             7
        from master                         0  (_merge==1)
        from using                          7  (_merge==2)

    Matched                               130  (_merge==3)
    -----------------------------------------

. keep if _merge==3
(7 observations deleted)

. 
. // label var ge03 "vendor ID - unique only within locality"
. gen vDescribe = m1q0d

. label var vDescribe "Describe location -- vendor"

. label var sample_repMkt "indicator for randomly selected vendor to represent
>  a locality, 1=Selected, 0=notSelected"
note: label truncated to 80 characters

. 
. // gen districtID = ge01
. // label var districtID "District code/ ID -- unique"
. 
. 
. // tostring loccode, gen(loccodex) format(%17.0g)
. 
. 
. order ge01 districtName ge02 ln vn ge03 vDescribe sample_repMkt

. tab ge01

   District |
       code |
  (4-digit) |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         12        9.23        9.23
          2 |          8        6.15       15.38
          3 |         10        7.69       23.08
          4 |         20       15.38       38.46
          5 |          7        5.38       43.85
          6 |         10        7.69       51.54
          7 |          9        6.92       58.46
          8 |         26       20.00       78.46
          9 |         28       21.54      100.00
------------+-----------------------------------
      Total |        130      100.00

. tab districtName

                  District |      Freq.     Percent        Cum.
---------------------------+-----------------------------------
                       PII |        130      100.00      100.00
---------------------------+-----------------------------------
                     Total |        130      100.00

. 
. // randtreat, generate(treatment) replace unequal(1/4 1/4 1/4 1/4) strata(ge
> 01) misfits(wstrata) setseed(12345)
. randtreat, generate(treatment) replace unequal(1/4 1/4 1/4 1/4) strata(distr
> ictID) misfits(wstrata) setseed(12345)
assignment produces 10 misfits

. tab treatment, miss

  treatment |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         32       24.62       24.62
          1 |         31       23.85       48.46
          2 |         32       24.62       73.08
          3 |         35       26.92      100.00
------------+-----------------------------------
      Total |        130      100.00

. tab ge01 treatment

  District |
      code |                  treatment
 (4-digit) |         0          1          2          3 |     Total
-----------+--------------------------------------------+----------
         1 |         3          3          3          3 |        12 
         2 |         2          2          2          2 |         8 
         3 |         2          2          3          3 |        10 
         4 |         5          5          5          5 |        20 
         5 |         2          2          1          2 |         7 
         6 |         3          2          2          3 |        10 
         7 |         2          2          2          3 |         9 
         8 |         6          6          7          7 |        26 
         9 |         7          7          7          7 |        28 
-----------+--------------------------------------------+----------
     Total |        32         31         32         35 |       130 

. save "$dta_loc_repl/01_intermediate/ONLY_4TrtGroups_9dist", replace
file
    /Users/yazenkashlan/Library/CloudStorage/OneDrive-Personal/Documents/per
    > sonal/Berk/03_Work/Francis/Replication/data_final/01_intermediate/ONLY
    > _4TrtGroups_9dist.dta saved

. 
. 
end of do-file

. do "$do_loc/_basel-interventions2"              // generate interventionsTom
> ake_list_local

. /*
> Interventions at the customer level (merged here)
> 
> Input:
>         - repMkt
>         - _CM_all_2_18
>         - ONLY_4TrtGroups_9dist
>         
> Output:
>         - interventionsTomake_list_local
>         
> */
. 
. 
. 
. 
. **ONLY: dta for office-Officers: intervention seeders/planters
. **launch to: only repVendors + only nearby-local? [all-global?] customers?
. use "$dta_loc_repl/01_intermediate/repMkt.dta", clear

. keep if sample_repMkt==1
(202 observations deleted)

. keep ge02 ge03 

. merge 1:m ge02 ge03 using  "$dta_loc_repl/00_raw_anon/_CM_all_2_18.dta"

    Result                      Number of obs
    -----------------------------------------
    Not matched                         1,008
        from master                         0  (_merge==1)
        from using                      1,008  (_merge==2)

    Matched                               990  (_merge==3)
    -----------------------------------------

. *merge 1:m loccode using  "_CM_all_2_18.dta"
. keep if _merge==3
(1,008 observations deleted)

. 
. bys loccode vendor_id: gen x=_N

. tab x

          x |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         16        1.62        1.62
          2 |         38        3.84        5.45
          3 |         48        4.85       10.30
          4 |         40        4.04       14.34
          5 |         20        2.02       16.36
          6 |         36        3.64       20.00
          7 |         35        3.54       23.54
          8 |         16        1.62       25.15
          9 |         27        2.73       27.88
         10 |         10        1.01       28.89
         11 |         44        4.44       33.33
         12 |        216       21.82       55.15
         13 |         78        7.88       63.03
         14 |         14        1.41       64.44
         15 |         75        7.58       72.02
         16 |         32        3.23       75.25
         17 |         51        5.15       80.40
         18 |         36        3.64       84.04
         20 |         20        2.02       86.06
         21 |         21        2.12       88.18
         23 |         92        9.29       97.47
         25 |         25        2.53      100.00
------------+-----------------------------------
      Total |        990      100.00

. 
. **let's check? very good...
. *bys loccode vendor_id: keep if _n==1
. *br
. keep ge02 ge03 ge04 custcode cn c1q0b c1q8a c1q8b

. tempfile ONLY_repMkt

. save    `ONLY_repMkt'
file /var/folders/6g/5g5fyd2d2p98vx66fbb08m1r0000gn/T//S_35344.000001 saved
    as .dta format

. 
. 
. use "$dta_loc_repl/01_intermediate/ONLY_4TrtGroups_9dist", clear

. merge 1:m ge02 ge03 using `ONLY_repMkt', gen(_mrep)

    Result                      Number of obs
    -----------------------------------------
    Not matched                             0
    Matched                               990  (_mrep==3)
    -----------------------------------------

. bys ge02 ge03: gen xx=_N

. tab xx //1-25 customers; only nearby customers that surround repMkt (not all
>  in locality possibly)

         xx |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         16        1.62        1.62
          2 |         38        3.84        5.45
          3 |         48        4.85       10.30
          4 |         40        4.04       14.34
          5 |         20        2.02       16.36
          6 |         36        3.64       20.00
          7 |         35        3.54       23.54
          8 |         16        1.62       25.15
          9 |         27        2.73       27.88
         10 |         10        1.01       28.89
         11 |         44        4.44       33.33
         12 |        216       21.82       55.15
         13 |         78        7.88       63.03
         14 |         14        1.41       64.44
         15 |         75        7.58       72.02
         16 |         32        3.23       75.25
         17 |         51        5.15       80.40
         18 |         36        3.64       84.04
         20 |         20        2.02       86.06
         21 |         21        2.12       88.18
         23 |         92        9.29       97.47
         25 |         25        2.53      100.00
------------+-----------------------------------
      Total |        990      100.00

. sum xx

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
          xx |        990    12.57374    6.184075          1         25

. tab treatment //ctr=185c, pt=272, mr=257, joint=276

  treatment |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        185       18.69       18.69
          1 |        272       27.47       46.16
          2 |        257       25.96       72.12
          3 |        276       27.88      100.00
------------+-----------------------------------
      Total |        990      100.00

. 
. **label vendor side??
. // label var vendor_id "vendor ID - unique only within locality"
. label var vn "vendor name"

. 
. **label customer side??
. gen customer_id = custcode

. label var customer_id "customer ID - unique only within locality"

. label var cn "customer name, nearby"

. gen cDescribe = c1q0b

. label var cDescribe "Describe location -- customer nearby"

. 
. **get things in strings for CAPI
. *tostring loccode, gen(loccodex) format(%17.0g)
. 
. 
. order ge01 districtName ge02 ln ///
>         vn ge03 vDescribe ///
>         cn customer_id cDescribe treatment

. 
. gen intervention =""
(990 missing values generated)

. replace intervention="Control" if treatment==0
variable intervention was str1 now str7
(185 real changes made)

. replace intervention="PriceTransparency, PT" if treatment==1
variable intervention was str7 now str21
(272 real changes made)

. replace intervention="MKtMonitoring, MM" if treatment==2
(257 real changes made)

. replace intervention="joint: PT+MM" if treatment==3
(276 real changes made)

. label var intervention "intervention or treatment type to implement"

. 
. keep ge01 districtName ge02 ln ///
>         vn ge03 vDescribe ///
>         cn customer_id ge04 cDescribe treatment intervention 

.         
. ** save
. saveold "$dta_loc_repl/01_intermediate/interventionsTomake_list_local", repl
> ace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in
      older Stata formats)
file
    /Users/yazenkashlan/Library/CloudStorage/OneDrive-Personal/Documents/per
    > sonal/Berk/03_Work/Francis/Replication/data_final/01_intermediate/inte
    > rventionsTomake_list_local.dta saved

. 
. 
end of do-file

. 
. ** combine
. do "$do_loc/_basel2-adminTransactData"  // generate [ofdrate_]mktadminTransa
> ctData

. /*
> Generate adminTransactData
> 
> Input:
>         - repMkt_w_xtics
>         - FFaudit
> Output:
>         - adminTransactData
>         
> */
. 
. 
. use "$dta_loc_repl/01_intermediate/repMkt_w_xtics", clear

. gen belief=1

. drop _merge

. keep if sample_repMkt==1
(931 observations deleted)

. bys ge02 ge03: keep if _n==1
(860 observations deleted)

. drop text* // text_ge* vars used below from FFaudit data

. 
. tempfile repMkt_w_VendorXtics

. save    `repMkt_w_VendorXtics'
file /var/folders/6g/5g5fyd2d2p98vx66fbb08m1r0000gn/T//S_35344.000001 saved
    as .dta format

. saveold "$dta_loc_repl/01_intermediate/repMkt_w_VendorXtics", replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in
      older Stata formats)
file
    /Users/yazenkashlan/Library/CloudStorage/OneDrive-Personal/Documents/per
    > sonal/Berk/03_Work/Francis/Replication/data_final/01_intermediate/repM
    > kt_w_VendorXtics.dta saved

. 
. 
.         
. use "$dta_loc_repl/00_raw_anon/FFaudit.dta", clear

. 
. bys ge02 ge03: gen xx=_N

. tab xx

         xx |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        129      100.00      100.00
------------+-----------------------------------
      Total |        129      100.00

. tab login

      LOGIN |      Freq.     Percent        Cum.
------------+-----------------------------------
         11 |         30       23.26       23.26
         21 |         31       24.03       47.29
         31 |         31       24.03       71.32
         41 |         37       28.68      100.00
------------+-----------------------------------
      Total |        129      100.00

. 
. merge 1:1 ge02 ge03 using `repMkt_w_VendorXtics'
(label _merge already defined)

    Result                      Number of obs
    -----------------------------------------
    Not matched                             1
        from master                         0  (_merge==1)
        from using                          1  (_merge==2)

    Matched                               129  (_merge==3)
    -----------------------------------------

. keep if _merge ==3
(1 observation deleted)

. 
. tab login

      LOGIN |      Freq.     Percent        Cum.
------------+-----------------------------------
         11 |         30       23.26       23.26
         21 |         31       24.03       47.29
         31 |         31       24.03       71.32
         41 |         37       28.68      100.00
------------+-----------------------------------
      Total |        129      100.00

. gen gender_auditor=.
(129 missing values generated)

. replace gender_auditor = 1 if (login==11) 
(30 real changes made)

. replace gender_auditor = 1 if (login==21) 
(31 real changes made)

. replace gender_auditor = 0 if (login==31) 
(31 real changes made)

. replace gender_auditor = 0 if (login==41) 
(37 real changes made)

. tab gender_auditor

gender_audi |
        tor |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         68       52.71       52.71
          1 |         61       47.29      100.00
------------+-----------------------------------
      Total |        129      100.00

. 
. 
. gen female=(ffaq12==2)

. replace female=. if missing(ffaq12)
(23 real changes made, 23 to missing)

. replace gender_auditor =. if missing(female)
(23 real changes made, 23 to missing)

. 
. gen tarrifpost=(ffaq9==1)

. replace tarrifpost=. if missing(ffaq9)
(23 real changes made, 23 to missing)

. 
. gen otherbus=(ffaq13==1)

. replace otherbus=. if missing(ffaq13)
(23 real changes made, 23 to missing)

. 
. **gender/matching frictions?
. gen gmatch=(female==gender_auditor)

. replace gmatch=. if missing(female)
(23 real changes made, 23 to missing)

. 
. **correctly: reshape data from wide to long -- randomization of transactions
. reshape long ffaq5_ ffaq6_ ffaq8_ ffaq0_, i(ge02) j(transact)  string
(j = 01 02 03 04 05 06 07 08 09 10 11 12)

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations              129   ->   1,548       
Number of variables                 469   ->   426         
j variable (12 values)                    ->   transact
xij variables:
         ffaq5_01 ffaq5_02 ... ffaq5_12   ->   ffaq5_
         ffaq6_01 ffaq6_02 ... ffaq6_12   ->   ffaq6_
         ffaq8_01 ffaq8_02 ... ffaq8_12   ->   ffaq8_
         ffaq0_01 ffaq0_02 ... ffaq0_12   ->   ffaq0_
-----------------------------------------------------------------------------

. 
. **liquidy shortfalls?
. tab ffaq0_ //47% of transactions unsuccessful

                   ffaq0_ |      Freq.     Percent        Cum.
--------------------------+-----------------------------------
    Transaction available |        663       52.12       52.12
Transaction Not available |        609       47.88      100.00
--------------------------+-----------------------------------
                    Total |      1,272      100.00

. tab status //reason: 11% of transactions=no cash in wallet

    Merchant Status |      Freq.     Percent        Cum.
--------------------+-----------------------------------
Available for Audit |      1,272       82.17       82.17
 Merchant relocated |         24        1.55       83.72
  No cash in wallet |        156       10.08       93.80
              Other |         96        6.20      100.00
--------------------+-----------------------------------
              Total |      1,548      100.00

. 
. **fin misconduct or fin-fraud?
. egen distrFes = group(ge01)

. egen vFes = group(ge02) //within-person?

. egen trFes = group(transact) //within-transaction?

. 
. **some xtics?
. gen vAge= m1q4
(12 missing values generated)

. gen vMarried=(m1q3==2)

. gen vYrsInbus =m2q1a
(12 missing values generated)

. gen vSizebus =m2q4b
(12 missing values generated)

. 
. **knowlegeable merchants vs non-k
. gen soph_m = (m_deviations==0)

. gen soph_c = (c_deviations==0)

. 
. *keep ffaudits_id transact ffaq1 ffaq3 ffaq5_ ffaq6_ ffaq8_ gender_auditor f
> emale tarrifpost otherbus gmatch trFes trXdateFes
. gen fYes_T = (ffaq5_==1)

.         replace fYes_T=. if missing(ffaq5_)
(885 real changes made, 885 to missing)

. gen fAmt_T = ffaq6_
(1,397 missing values generated)

.         replace fAmt_T=. if missing(ffaq6_)
(0 real changes made)

.         replace fAmt_T=fAmt_T/10 if fAmt_T>10   //an adjustments from the fi
> eld
(88 real changes made)

. gen wTime_T = ffaq8_
(885 missing values generated)

.         replace wTime_T=. if missing(ffaq8_)
(0 real changes made)

.         
. **group/ class transactions?
. gen tranType = " "

. replace tranType = "OTC-base: 01-03" if (transact=="01" | transact=="02" | t
> ransact=="03")
variable tranType was str1 now str15
(387 real changes made)

. replace tranType = "OTC-token: 04-07" if (transact=="04" | transact=="05" | 
> transact=="06" | transact=="07")
variable tranType was str15 now str16
(516 real changes made)

. replace tranType = "Falsification: 08-10" if (transact=="08" | transact=="09
> " | transact=="10")
variable tranType was str16 now str20
(387 real changes made)

. replace tranType = "Open-account: 11-12" if (transact=="11" | transact=="12"
> )
(258 real changes made)

. 
. gen round = 1

. drop _merge

. 
. **misconduct: summaries?
. gen sv_fAmt_T = fAmt_T
(1,397 missing values generated)

. 
. gen transactK = transact

. replace transactK ="01 Cash-in GHS50 - others wallet" if transact=="01"
variable transactK was str2 now str32
(129 real changes made)

. replace transactK ="02 Cash-in GHS160 - others wallet" if transact=="02"
variable transactK was str32 now str33
(129 real changes made)

. replace transactK ="03 Cash-in GHS1100 - others wallet" if transact=="03"
variable transactK was str33 now str34
(129 real changes made)

. replace transactK ="04 Send GHS50 token - others" if transact=="04"
(129 real changes made)

. replace transactK ="05 Send GHS1100 token - others" if transact=="05"
(129 real changes made)

. replace transactK ="06 Receive GHS50 token" if transact=="06"
(129 real changes made)

. replace transactK ="07 Receive GHS1100 token" if transact=="07"
(129 real changes made)

. replace transactK ="08 Cash-in GHS50 - own wallet" if transact=="08"
(129 real changes made)

. replace transactK ="09 Cash-in GHS160 - own wallet" if transact=="09"
(129 real changes made)

. replace transactK ="10 Cash-out GHS50 - own wallet" if transact=="10"
(129 real changes made)

. replace transactK ="11 Purchase new SIM card" if transact=="11"
(129 real changes made)

. replace transactK ="12 Register new M-Money wallet" if transact=="12"
(129 real changes made)

. 
. **QUESTION?
. **(intentional) misconduct or (innocent) errors? 
. **then should see average = 0 rel to mandated rate, and no asymmetry
. gen devAmt = fAmt_T
(1,397 missing values generated)

. replace devAmt=0 if fYes_T==0
(512 real changes made)

. 
. 
. **Indentif Strategy: Confounds?: intuition
. **I] Between results: Fes here capture unob diffs based on loc. / transact C
> ycles / (robustly) transact Type?
. **comparing two M-M transactions carried out within the same day and distric
> t (or village-rep's comparable)**
. 
. 
. save "$dta_loc_repl/01_intermediate/adminTransactData", replace
file
    /Users/yazenkashlan/Library/CloudStorage/OneDrive-Personal/Documents/per
    > sonal/Berk/03_Work/Francis/Replication/data_final/01_intermediate/admi
    > nTransactData.dta saved

. 
. 
. **quantifying: "Bias belief vs direct Price Effects"? 
. *2. bring in objective misconduct at t0?
. **intermediate step- get baseline objective fraud
. ** Source: Stats?/Commands_Test_f_evaluation_consumers.do
. 
. tab fYes_T

     fYes_T |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        512       77.22       77.22
          1 |        151       22.78      100.00
------------+-----------------------------------
      Total |        663      100.00

. gen sv_fAmt_T0 = sv_fAmt_T
(1,397 missing values generated)

. replace sv_fAmt_T0=0 if fYes_T==0
(512 real changes made)

. 
. ** These calculations, unlike merges above, are done using text IDs
. bys text_ge01 text_ge02: egen obj_fd_t0 = mean(fYes_T) //continuous, measure
>  mkt=rep.vendor
(276 missing values generated)

. replace obj_fd_t0 = obj_fd_t0*100 
(876 real changes made)

. bys text_ge01 text_ge02: egen obj_fdamt_t0 = mean(sv_fAmt_T0) //continuous, 
> mkt=rep.vendor
(276 missing values generated)

. pwcorr obj_fd_t0 obj_fdamt_t0, sig

             | obj~d_t0 obj~t_t0
-------------+------------------
   obj_fd_t0 |   1.0000 
             |
             |
obj_fdamt_t0 |   0.9317   1.0000 
             |   0.0000
             |

. 
. bys text_ge01 text_ge02: keep if _n==1
(1,419 observations deleted)

. hist obj_fd_t0, frac
(bin=10, start=0, width=10)

. hist obj_fdamt_t0, frac
(bin=10, start=0, width=.28333333)

. sum obj_fd_t0 obj_fdamt_t0, d

                          obj_fd_t0
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                 106
25%            0              0       Sum of wgt.         106

50%           20                      Mean           23.05518
                        Largest       Std. dev.      20.92293
75%           40             60
90%           50           62.5       Variance       437.7691
95%           60       71.42857       Skewness       .7118283
99%     71.42857            100       Kurtosis       3.332332

                        obj_fdamt_t0
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                 106
25%            0              0       Sum of wgt.         106

50%     .7083333                      Mean           .7849543
                        Largest       Std. dev.      .7711459
75%     1.333333           2.25
90%          1.8            2.3       Variance        .594666
95%            2            2.8       Skewness       .6352915
99%          2.8       2.833333       Kurtosis       2.306454

. gen fdH0_t0 = (obj_fd_t0>0) if !missing(obj_fd_t0) //binary measure (above 0
> %)
(23 missing values generated)

. gen fdH1_t0 = (obj_fd_t0>20) if !missing(obj_fd_t0) //binary (above median=2
> 0% vs endl=14.2%)
(23 missing values generated)

. gen fdamtH0_t0 = (obj_fdamt_t0>0) if !missing(obj_fdamt_t0)
(23 missing values generated)

. gen fdamtH1_t0 = (obj_fdamt_t0>0.708) if !missing(obj_fdamt_t0) //(above med
> ian=0.708ghS vs endl=0.412ghS)
(23 missing values generated)

. keep text_ge01 text_ge02 obj_fd_t0 obj_fdamt_t0 fdH* fdamtH*

. // drop if text_ge01 == . // drop missing to ensure vars are IDs
. // isid text_ge01 text_ge02
. 
. saveold "$dta_loc_repl/01_intermediate/ofdrate_mktadminTransactData", replac
> e
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in
      older Stata formats)
file
    /Users/yazenkashlan/Library/CloudStorage/OneDrive-Personal/Documents/per
    > sonal/Berk/03_Work/Francis/Replication/data_final/01_intermediate/ofdr
    > ate_mktadminTransactData.dta saved

. 
. 
end of do-file

. do "$do_loc/_basel2-combine"                    // combine customer/merchant
>  + int + mkt census (commented out sqreg and gen. item=y)

. /*
> Combine baseline and raw datasets
> 
> Source: Commands_Test_f_evaluation_consumers.do
>                 The output dataset is also generated in Commands_Test_f_eval
> uation
>                 but that is outdated.
> Input: 
>         - Customer
>         - Mkt_census_xtics_+_interventions_localized
> Output:
>         - Customer_+_Mktcensus_+_Interventions
>         
> */
. 
. ** -------------------------------------------------------------------------
> ----
. **I--Mkt Census xtics + Interventions (localized)?
. use "$dta_loc_repl/01_intermediate/Mkt_fieldData_census", clear

. drop _merge

. merge m:1 ge03 ge04 using "$dta_loc_repl/01_intermediate/interventionsTomake
> _list_local" //customers match subsumes vednors//

    Result                      Number of obs
    -----------------------------------------
    Not matched                         1,064
        from master                     1,064  (_merge==1)
        from using                          0  (_merge==2)

    Matched                               990  (_merge==3)
    -----------------------------------------

. keep if _merge==3
(1,064 observations deleted)

. drop _merge

. tempfile Mkt_census_xtics_int_lclzd

. save    `Mkt_census_xtics_int_lclzd'
file /var/folders/6g/5g5fyd2d2p98vx66fbb08m1r0000gn/T//S_35344.000001 saved
    as .dta format

. 
. 
. 
. ** -------------------------------------------------------------------------
> ----
. *Customer analysis?
. **************
. ***************
. use "$dta_loc_repl/00_raw_anon/Customer_corrected.dta", clear

. merge 1:1 ge04 using `Mkt_census_xtics_int_lclzd'
(label _merge already defined)

    Result                      Number of obs
    -----------------------------------------
    Not matched                           180
        from master                         0  (_merge==1)
        from using                        180  (_merge==2)

    Matched                               810  (_merge==3)
    -----------------------------------------

. 
. ** generate loccode for convenience
. gen loccode = ge02

. 
. *keep if _merge ==3
. *drop if _n>950
. 
. **attrition stats: numbers
. tab intervention

      intervention or |
    treatment type to |
            implement |      Freq.     Percent        Cum.
----------------------+-----------------------------------
              Control |        185       18.69       18.69
    MKtMonitoring, MM |        257       25.96       44.65
PriceTransparency, PT |        272       27.47       72.12
         joint: PT+MM |        276       27.88      100.00
----------------------+-----------------------------------
                Total |        990      100.00

. gen dropouts = (_merge==2)

. tab intervention if dropouts==0

      intervention or |
    treatment type to |
            implement |      Freq.     Percent        Cum.
----------------------+-----------------------------------
              Control |        143       17.65       17.65
    MKtMonitoring, MM |        207       25.56       43.21
PriceTransparency, PT |        230       28.40       71.60
         joint: PT+MM |        230       28.40      100.00
----------------------+-----------------------------------
                Total |        810      100.00

. *get mean=% and SD=%?
. gen ins=(dropouts==0)

. tabstat ins, stat(mean sd n) by(intervention) // Table B.5

Summary for variables: ins
Group variable: intervention (intervention or treatment type to implement)

    intervention |      Mean        SD         N
-----------------+------------------------------
         Control |   .772973   .420047       185
MKtMonitoring, M |  .8054475  .3966282       257
PriceTransparenc |  .8455882  .3620091       272
    joint: PT+MM |  .8333333   .373355       276
-----------------+------------------------------
           Total |  .8181818  .3858896       990
------------------------------------------------

. tabstat dropouts, stat(mean sd n) by(intervention)

Summary for variables: dropouts
Group variable: intervention (intervention or treatment type to implement)

    intervention |      Mean        SD         N
-----------------+------------------------------
         Control |   .227027   .420047       185
MKtMonitoring, M |  .1945525  .3966282       257
PriceTransparenc |  .1544118  .3620091       272
    joint: PT+MM |  .1666667   .373355       276
-----------------+------------------------------
           Total |  .1818182  .3858896       990
------------------------------------------------

. 
. 
. 
. *drop if missing(c1a2)
. 
. 
. tab intervention

      intervention or |
    treatment type to |
            implement |      Freq.     Percent        Cum.
----------------------+-----------------------------------
              Control |        185       18.69       18.69
    MKtMonitoring, MM |        257       25.96       44.65
PriceTransparency, PT |        272       27.47       72.12
         joint: PT+MM |        276       27.88      100.00
----------------------+-----------------------------------
                Total |        990      100.00

. gen trtment = (intervention != "Control")

. 
. gen trtment_mm =.
(990 missing values generated)

. replace trtment_mm=1 if (intervention == "MKtMonitoring, MM")
(257 real changes made)

. replace trtment_mm=0 if (intervention == "Control")
(185 real changes made)

. 
. gen trtment_pt=.
(990 missing values generated)

. replace trtment_pt=1 if (intervention == "PriceTransparency, PT")
(272 real changes made)

. replace trtment_pt=0 if (intervention == "Control")
(185 real changes made)

. 
. gen trtment_mmpt=.
(990 missing values generated)

. replace trtment_mmpt=1 if (intervention == "joint: PT+MM")
(276 real changes made)

. replace trtment_mmpt=0 if (intervention == "Control")
(185 real changes made)

. 
. gen trt=0

. replace trt=1 if intervention=="PriceTransparency, PT"
(272 real changes made)

. replace trt=2 if intervention=="MKtMonitoring, MM"
(257 real changes made)

. replace trt=3 if intervention=="joint: PT+MM"
(276 real changes made)

. 
. sum trt*

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     trtment |        990    .8131313    .3900031          0          1
  trtment_mm |        442     .581448    .4938806          0          1
  trtment_pt |        457     .595186    .4913939          0          1
trtment_mmpt |        461    .5986985    .4906943          0          1
         trt |        990    1.630303    1.079589          0          3

. egen xloc =group(loccode)

. *tab xloc
. 
. gen trt_pool = (trt !=0)

. 
. 
. *distplot c0a, saving("distplot_ccalls", replace) //customers answer quicker
>  than vendors/business (as expected)
. *hist c0a, percent xtitle("Customers: Number of phone call times before answ
> ering survey")
. *gr export "/Users/fannan/Dropbox/research_projs/fraud-monitors/_rGroup-finf
> raud/FFPhone in 2020/_impact-evaluation/customer_calltimeS.eps", replace
. 
. 
. **(1) differential attrition/ drop outs?
. tab _merge

   Matching result from |
                  merge |      Freq.     Percent        Cum.
------------------------+-----------------------------------
         Using only (2) |        180       18.18       18.18
            Matched (3) |        810       81.82      100.00
------------------------+-----------------------------------
                  Total |        990      100.00

. *ciplot dropouts, by(trtment) title("differential attrition?")
. *ciplot dropouts, by(trt) title("differential attrition?")
. bys trtment: sum dropouts 

------------------------------------------------------------------------------
-> trtment = 0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    dropouts |        185     .227027     .420047          0          1

------------------------------------------------------------------------------
-> trtment = 1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    dropouts |        805    .1714286    .3771173          0          1


. dis 0.23-0.18 //control has 5pp higher attrition, responserate for treatment
> =0.82=82% 
.05

. tab dropouts if trtment==0

   dropouts |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        143       77.30       77.30
          1 |         42       22.70      100.00
------------+-----------------------------------
      Total |        185      100.00

. tab dropouts if trtment==1

   dropouts |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        667       82.86       82.86
          1 |        138       17.14      100.00
------------+-----------------------------------
      Total |        805      100.00

. **so trim 0.05/0.82 = 6.1% of treatment group
. **764 responses, so triming 46 customers
. 
. *gen item= y if trtment==1
. *gen iranklo_a =rank(item) if trtment==1, unique
. *gen iranklo_b =rank(-item) if trtment==1, unique
. *gen yupper= y
. *replace yupper=. if trtment==1 & iranklo_a<=46
. *gen ylower= y
. *replace ylower=. if trtment==1 & iranklo_b<=46
. *areg ylower trtment, a(districtID) robust
. *areg yupper trtment, a(districtID) robust
. 
. 
. bys trt: sum dropouts 

------------------------------------------------------------------------------
-> trt = 0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    dropouts |        185     .227027     .420047          0          1

------------------------------------------------------------------------------
-> trt = 1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    dropouts |        272    .1544118    .3620091          0          1

------------------------------------------------------------------------------
-> trt = 2

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    dropouts |        257    .1945525    .3966282          0          1

------------------------------------------------------------------------------
-> trt = 3

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    dropouts |        276    .1666667     .373355          0          1


. 
. 
. **(2) balanced?
. *3a Demand: customer xtics, same mkt?
. ** xtics? married out...
. reg cfemale dropouts, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(1, 129)         =       0.08
                                                Prob > F          =     0.7792
                                                R-squared         =     0.0001
                                                Root MSE          =      .4841

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
     cfemale | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |   .0121802   .0433557     0.28   0.779    -.0736001    .0979604
       _cons |   .6246914   .0219739    28.43   0.000     .5812155    .6681672
------------------------------------------------------------------------------

. reg cmarried dropouts, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(1, 129)         =       2.38
                                                Prob > F          =     0.1255
                                                R-squared         =     0.0025
                                                Root MSE          =     .49837

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
    cmarried | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |  -.0645838   .0418853    -1.54   0.126    -.1474548    .0182872
       _cons |   .5506173   .0200495    27.46   0.000     .5109489    .5902856
------------------------------------------------------------------------------

. reg cakan dropouts, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(1, 129)         =       0.29
                                                Prob > F          =     0.5891
                                                R-squared         =     0.0004
                                                Root MSE          =     .48508

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
       cakan | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |   .0239396   .0442052     0.54   0.589    -.0635214    .1114006
       _cons |   .6185185   .0331199    18.68   0.000       .55299     .684047
------------------------------------------------------------------------------

. reg cage dropouts, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(1, 129)         =       1.55
                                                Prob > F          =     0.2158
                                                R-squared         =     0.0018
                                                Root MSE          =     15.212

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
        cage | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |  -1.665032   1.338584    -1.24   0.216    -4.313454    .9833901
       _cons |   40.62593   .6723274    60.43   0.000     39.29571    41.95614
------------------------------------------------------------------------------

. reg cEducAny dropouts, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(1, 129)         =       0.69
                                                Prob > F          =     0.4081
                                                R-squared         =     0.0007
                                                Root MSE          =     .30034

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
    cEducAny | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |   .0199048   .0239813     0.83   0.408    -.0275427    .0673523
       _cons |   .8962963   .0144773    61.91   0.000     .8676527    .9249399
------------------------------------------------------------------------------

. reg cselfemployed dropouts, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(1, 129)         =       2.02
                                                Prob > F          =     0.1580
                                                R-squared         =     0.0020
                                                Root MSE          =     .46184

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
cselfemplo~d | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |  -.0531899    .037453    -1.42   0.158    -.1272916    .0209119
       _cons |   .7012346    .023254    30.16   0.000      .655226    .7472432
------------------------------------------------------------------------------

. reg cselfIncome dropouts, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(1, 129)         =       0.15
                                                Prob > F          =     0.7016
                                                R-squared         =     0.0002
                                                Root MSE          =     .76215

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
 cselfIncome | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |  -.0243741   .0634607    -0.38   0.702    -.1499326    .1011844
       _cons |   1.303704   .0439821    29.64   0.000     1.216684    1.390723
------------------------------------------------------------------------------

. reg cMMoneyregistered dropouts, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(1, 129)         =       6.55
                                                Prob > F          =     0.0117
                                                R-squared         =     0.0198
                                                Root MSE          =     .29203

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
cMMoneyreg~d | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |  -.1078143   .0421364    -2.56   0.012    -.1911823   -.0244464
       _cons |   .9234568     .01026    90.01   0.000     .9031571    .9437564
------------------------------------------------------------------------------

. 
. **migrate?
. gen migrateDesire= (c7q1==1)

. gen migratein1yr = (c7q3 <3)

. gen migratepermanent = (c7q4 ==2)

. factor migrateDesire migratein1yr migratepermanent
(obs=990)

Factor analysis/correlation                      Number of obs    =        990
    Method: principal factors                    Retained factors =          2
    Rotation: (unrotated)                        Number of params =          3

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      1.65439      1.63086            1.1272       1.1272
        Factor2  |      0.02352      0.23374            0.0160       1.1432
        Factor3  |     -0.21021            .           -0.1432       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(3)  = 1106.56 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    -------------------------------------------------
        Variable |  Factor1   Factor2 |   Uniqueness 
    -------------+--------------------+--------------
    migrateDes~e |   0.8528    0.0001 |      0.2728  
    migratein1yr |   0.6750    0.1093 |      0.5325  
    migrateper~t |   0.6867   -0.1076 |      0.5168  
    -------------------------------------------------

. predict migrate_score_c
(option regression assumed; regression scoring)

Scoring coefficients (method = regression)

    ----------------------------------
        Variable |  Factor1   Factor2 
    -------------+--------------------
    migrateDes~e |  0.55996   0.00346 
    migratein1yr |  0.21899   0.18146 
    migrateper~t |  0.22839  -0.18348 
    ----------------------------------


. reg migrateDesire dropouts, cluster(loccode)

Linear regression                               Number of obs     =        990
                                                F(1, 129)         =       0.02
                                                Prob > F          =     0.8881
                                                R-squared         =     0.0000
                                                Root MSE          =     .47599

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
migrateDes~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |   .0055556   .0394056     0.14   0.888    -.0724093    .0835205
       _cons |   .3444444   .0305231    11.28   0.000     .2840538    .4048351
------------------------------------------------------------------------------

. reg migratein1yr dropouts, cluster(loccode)

Linear regression                               Number of obs     =        990
                                                F(1, 129)         =       0.36
                                                Prob > F          =     0.5496
                                                R-squared         =     0.0003
                                                Root MSE          =     .38602

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
migratein1yr | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |  -.0185185   .0308658    -0.60   0.550    -.0795872    .0425502
       _cons |   .1851852   .0183524    10.09   0.000     .1488746    .2214958
------------------------------------------------------------------------------

. reg migratepermanent dropouts, cluster(loccode)

Linear regression                               Number of obs     =        990
                                                F(1, 129)         =       0.13
                                                Prob > F          =     0.7147
                                                R-squared         =     0.0001
                                                Root MSE          =     .39018

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
migrateper~t | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |  -.0111111   .0303268    -0.37   0.715    -.0711134    .0488912
       _cons |   .1888889   .0238225     7.93   0.000     .1417554    .2360224
------------------------------------------------------------------------------

. reg migrate_score_c dropouts, cluster(loccode)

Linear regression                               Number of obs     =        990
                                                F(1, 129)         =       0.02
                                                Prob > F          =     0.8849
                                                R-squared         =     0.0000
                                                Root MSE          =     .88485

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
migrate_sc~c | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |  -.0104773   .0722421    -0.15   0.885      -.15341    .1324554
       _cons |    .001905   .0555156     0.03   0.973    -.1079339    .1117439
------------------------------------------------------------------------------

. 
. 
. **poverty?
. reg c2q1 dropouts, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(1, 129)         =       1.82
                                                Prob > F          =     0.1798
                                                R-squared         =     0.0018
                                                Root MSE          =     8.7533

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
        c2q1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |   .9704669   .7196222     1.35   0.180    -.4533231    2.394257
       _cons |   15.15802   .3914334    38.72   0.000     14.38356    15.93249
------------------------------------------------------------------------------

. *reg c2q2 dropouts, cluster(loccode)
. reg c2q3 dropouts, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(1, 129)         =       2.71
                                                Prob > F          =     0.1019
                                                R-squared         =     0.0033
                                                Root MSE          =     2.1593

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
        c2q3 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |  -.3238568    .196579    -1.65   0.102    -.7127933    .0650796
       _cons |   3.362963   .1368443    24.58   0.000     3.092213    3.633713
------------------------------------------------------------------------------

. reg c2q4 dropouts, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(1, 129)         =       1.63
                                                Prob > F          =     0.2033
                                                R-squared         =     0.0018
                                                Root MSE          =     2.3356

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
        c2q4 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |  -.2539141   .1985841    -1.28   0.203    -.6468175    .1389894
       _cons |   3.438272   .1690315    20.34   0.000     3.103839    3.772705
------------------------------------------------------------------------------

. reg c2q5 dropouts, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(1, 129)         =       0.50
                                                Prob > F          =     0.4824
                                                R-squared         =     0.0006
                                                Root MSE          =     2.0835

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
        c2q5 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |   -.136368   .1935636    -0.70   0.482    -.5193384    .2466023
       _cons |   3.812346   .1714348    22.24   0.000     3.473158    4.151534
------------------------------------------------------------------------------

. *reg c2q6 dropouts, cluster(loccode)
. *reg c2q7 dropouts, cluster(loccode)
. *reg c2q8 dropouts, cluster(loccode)
. reg c2q9 dropouts, cluster(loccode)

Linear regression                               Number of obs     =        988
                                                F(1, 128)         =       4.03
                                                Prob > F          =     0.0469
                                                R-squared         =     0.0073
                                                Root MSE          =     2.6585

                              (Std. err. adjusted for 129 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
        c2q9 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |  -.5931475   .2955879    -2.01   0.047    -1.178019   -.0082763
       _cons |   7.098765   .1151921    61.63   0.000     6.870838    7.326693
------------------------------------------------------------------------------

. reg c2q10 dropouts, cluster(loccode)

Linear regression                               Number of obs     =        988
                                                F(1, 128)         =       0.51
                                                Prob > F          =     0.4746
                                                R-squared         =     0.0007
                                                Root MSE          =     2.8575

                              (Std. err. adjusted for 129 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
       c2q10 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |   .1947011   .2715026     0.72   0.475    -.3425132    .7319154
       _cons |   1.383951   .1313541    10.54   0.000     1.124044    1.643857
------------------------------------------------------------------------------

. reg c_pov_likelihood dropouts, cluster(loccode)

Linear regression                               Number of obs     =        990
                                                F(1, 129)         =       1.43
                                                Prob > F          =     0.2338
                                                R-squared         =     0.0019
                                                Root MSE          =     14.732

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
c_pov_like~d | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |   1.670062   1.396047     1.20   0.234    -1.092052    4.432175
       _cons |   12.19383   .9030124    13.50   0.000      10.4072    13.98046
------------------------------------------------------------------------------

. 
. **fraud?
. reg cfAttempts dropouts, cluster(loccode)

Linear regression                               Number of obs     =        988
                                                F(1, 128)         =       1.47
                                                Prob > F          =     0.2276
                                                R-squared         =     0.0016
                                                Root MSE          =     .49521

                              (Std. err. adjusted for 129 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
  cfAttempts | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |  -.0509225   .0420021    -1.21   0.228    -.1340307    .0321858
       _cons |   .5790123   .0262005    22.10   0.000     .5271701    .6308546
------------------------------------------------------------------------------

. reg _Xcfraud dropouts, cluster(loccode)

Linear regression                               Number of obs     =        988
                                                F(1, 128)         =       0.25
                                                Prob > F          =     0.6155
                                                R-squared         =     0.0002
                                                Root MSE          =     .46111

                              (Std. err. adjusted for 129 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
    _Xcfraud | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |   .0177556   .0352675     0.50   0.616    -.0520271    .0875383
       _cons |   .3024691   .0202814    14.91   0.000      .262339    .3425993
------------------------------------------------------------------------------

. 
. *3b mkt, transactions?
. gen distToBank= c3q3a 
(818 missing values generated)

. gen walkTimeBank= c3q3b 
(818 missing values generated)

. gen bankUser = (c3q4==1)

. replace bankUser=. if missing(c3q4)
(818 real changes made, 818 to missing)

. 
. gen distTopostOffice = c3q7a
(947 missing values generated)

. gen walkTimepostOffice = c3q7b
(947 missing values generated)

. gen postOffUser=(c3q8==1)

. replace postOffUser=. if missing(c3q8)
(947 real changes made, 947 to missing)

. 
. gen distToMMoney= c4q2a
(65 missing values generated)

. gen walkTimeMMoney= c4q2b
(65 missing values generated)

. gen MMoneyUser=(c4q3==1)

. replace MMoneyUser=. if missing(c4q3)
(65 real changes made, 65 to missing)

. 
. reg distToBank dropouts, cluster(loccode)

Linear regression                               Number of obs     =        172
                                                F(1, 59)          =       3.64
                                                Prob > F          =     0.0613
                                                R-squared         =     0.0047
                                                Root MSE          =     1102.8

                               (Std. err. adjusted for 60 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
  distToBank | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |  -226.1727   118.5803    -1.91   0.061    -463.4513    11.10585
       _cons |      462.9   107.9796     4.29   0.000     246.8333    678.9667
------------------------------------------------------------------------------

. reg distToMMoney dropouts, cluster(loccode)

Linear regression                               Number of obs     =        925
                                                F(1, 126)         =       2.70
                                                Prob > F          =     0.1031
                                                R-squared         =     0.0030
                                                Root MSE          =     85.301

                              (Std. err. adjusted for 127 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
distToMMoney | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |  -12.55794   7.648846    -1.64   0.103    -27.69478      2.5789
       _cons |    57.6541   7.434628     7.75   0.000     42.94119    72.36701
------------------------------------------------------------------------------

. 
. *reg wklyNobUsage dropouts, cluster(loccode)
. reg wklyTotUseVol dropouts, cluster(loccode)

Linear regression                               Number of obs     =        925
                                                F(1, 126)         =       1.09
                                                Prob > F          =     0.2975
                                                R-squared         =     0.0040
                                                Root MSE          =     462.88

                              (Std. err. adjusted for 127 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
wklyTotUse~l | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |   77.82151   74.39468     1.05   0.298    -69.40337    225.0464
       _cons |   145.3836   11.82917    12.29   0.000      121.974    168.7932
------------------------------------------------------------------------------

. reg wklyNobUsage_nonM dropouts, cluster(loccode)

Linear regression                               Number of obs     =        988
                                                F(1, 128)         =       0.03
                                                Prob > F          =     0.8572
                                                R-squared         =     0.0000
                                                Root MSE          =     17.479

                              (Std. err. adjusted for 129 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
wklyNobUsa~M | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |  -.1626717   .9021875    -0.18   0.857    -1.947804     1.62246
       _cons |   2.522222   .6745246     3.74   0.000      1.18756    3.856884
------------------------------------------------------------------------------

. reg wklyTotUseVol_nonM dropouts, cluster(loccode)

Linear regression                               Number of obs     =        988
                                                F(1, 128)         =      12.22
                                                Prob > F          =     0.0007
                                                R-squared         =     0.0031
                                                Root MSE          =     277.89

                              (Std. err. adjusted for 129 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
wklyTotUse~M | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |  -40.26208   11.51732    -3.50   0.001    -63.05106   -17.47309
       _cons |   52.95309   11.58914     4.57   0.000     30.02199    75.88419
------------------------------------------------------------------------------

. **get distribution effects-main? which bound is more likely?
. *sqreg wklyTotUseVol dropouts, q(.25 .5 .75)
. 
. 
. *3c borrow + save behavior?
. gen likelyborrowMMoney =c5q1
(2 missing values generated)

. gen likelysaveMMoney =c5q5
(2 missing values generated)

. reg likelyborrowMMoney dropouts, cluster(loccode)

Linear regression                               Number of obs     =        988
                                                F(1, 128)         =       0.05
                                                Prob > F          =     0.8269
                                                R-squared         =     0.0001
                                                Root MSE          =     .90889

                              (Std. err. adjusted for 129 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
likelyborr~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |   .0193786   .0884127     0.22   0.827    -.1555611    .1943182
       _cons |   1.446914   .0545748    26.51   0.000     1.338928    1.554899
------------------------------------------------------------------------------

. reg likelysaveMMoney dropouts, cluster(loccode)

Linear regression                               Number of obs     =        988
                                                F(1, 128)         =       0.03
                                                Prob > F          =     0.8599
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2643

                              (Std. err. adjusted for 129 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
likelysave~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dropouts |  -.0222084    .125567    -0.18   0.860    -.2706642    .2262475
       _cons |   2.134568    .092158    23.16   0.000     1.952218    2.316918
------------------------------------------------------------------------------

. **get distribution effects-main? which bound is more likely?
. *sqreg likelysaveMMoney dropouts, q(.25 .5 .75)
. 
. /* 
> reg wklyNobBorrow dropouts, cluster(loccode)
> reg wklyTotBorrowVol dropouts, cluster(loccode)
> reg wklyNobSave dropouts, cluster(loccode)
> reg wklyTotSaveVol dropouts, cluster(loccode)
> */
. **joint, exclude main Y?
. reg dropouts cfemale cmarried cakan cage cEducAny cselfemployed cselfIncome 
> cMMoneyregistered, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(8, 129)         =       2.37
                                                Prob > F          =     0.0205
                                                R-squared         =     0.0265
                                                Root MSE          =     .38161

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
    dropouts | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     cfemale |   .0019096   .0273213     0.07   0.944    -.0521463    .0559655
    cmarried |  -.0212079   .0246537    -0.86   0.391    -.0699858      .02757
       cakan |   .0236941   .0272207     0.87   0.386    -.0301628     .077551
        cage |  -.0010833   .0007756    -1.40   0.165    -.0026179    .0004513
    cEducAny |   .0240643   .0376767     0.64   0.524    -.0504801    .0986087
cselfemplo~d |  -.0287733   .0264463    -1.09   0.279    -.0810979    .0235513
 cselfIncome |  -.0003127   .0164504    -0.02   0.985    -.0328602    .0322348
cMMoneyreg~d |  -.1926039   .0610931    -3.15   0.002    -.3134781   -.0717297
       _cons |   .3929024   .0867888     4.53   0.000     .2211887    .5646161
------------------------------------------------------------------------------

. test cfemale cmarried cakan cage cEducAny cselfemployed cselfIncome cMMoneyr
> egistered

 ( 1)  cfemale = 0
 ( 2)  cmarried = 0
 ( 3)  cakan = 0
 ( 4)  cage = 0
 ( 5)  cEducAny = 0
 ( 6)  cselfemployed = 0
 ( 7)  cselfIncome = 0
 ( 8)  cMMoneyregistered = 0

       F(  8,   129) =    2.37
            Prob > F =    0.0205

. probit dropouts cfemale cakan cmarried cage cEducAny cselfemployed cselfInco
> me cMMoneyregistered, cluster(loccode)

Iteration 0:  Log pseudolikelihood = -467.69089  
Iteration 1:  Log pseudolikelihood = -455.80868  
Iteration 2:  Log pseudolikelihood = -455.75624  
Iteration 3:  Log pseudolikelihood = -455.75624  

Probit regression                                       Number of obs =    989
                                                        Wald chi2(8)  =  21.58
                                                        Prob > chi2   = 0.0058
Log pseudolikelihood = -455.75624                       Pseudo R2     = 0.0255

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
    dropouts | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     cfemale |   .0031669   .1074286     0.03   0.976    -.2073894    .2137231
       cakan |   .0896629   .1073793     0.84   0.404    -.1207966    .3001224
    cmarried |  -.0854225   .0933685    -0.91   0.360    -.2684215    .0975764
        cage |  -.0045605    .003098    -1.47   0.141    -.0106326    .0015115
    cEducAny |   .0897985   .1646368     0.55   0.585    -.2328836    .4124806
cselfemplo~d |  -.1067983   .0977946    -1.09   0.275    -.2984721    .0848756
 cselfIncome |  -.0008141   .0678391    -0.01   0.990    -.1337763    .1321482
cMMoneyreg~d |  -.6305774   .1732617    -3.64   0.000    -.9701641   -.2909908
       _cons |  -.1959419   .3085721    -0.63   0.525    -.8007322    .4088483
------------------------------------------------------------------------------

. test cfemale cmarried cakan cage cEducAny cselfemployed cselfIncome cMMoneyr
> egistered

 ( 1)  [dropouts]cfemale = 0
 ( 2)  [dropouts]cmarried = 0
 ( 3)  [dropouts]cakan = 0
 ( 4)  [dropouts]cage = 0
 ( 5)  [dropouts]cEducAny = 0
 ( 6)  [dropouts]cselfemployed = 0
 ( 7)  [dropouts]cselfIncome = 0
 ( 8)  [dropouts]cMMoneyregistered = 0

           chi2(  8) =   21.58
         Prob > chi2 =    0.0058

. 
. 
. 
. **Measurements...
. gen mmUser_t1 = (c1a1 > 0) if _merge==3
(180 missing values generated)

. gen mmUser_t0=(c4q3==1)

. replace mmUser_t0=. if missing(c4q3)
(65 real changes made, 65 to missing)

. 
. 
. gen mmtotnob_t1 = c1a1
(227 missing values generated)

. gen mmtotnob_t0 = c4q11a
(65 missing values generated)

. 
. 
. gen log_mmtotamt_t1 = log(c1a2+1) if !missing(c1a2)
(227 missing values generated)

. gen log_mmtotamt_t0=log(c4q11b+1) if !missing(c4q11b)
(65 missing values generated)

. 
. 
. gen mmtotamt_t1 = c1a2
(227 missing values generated)

. gen mmtotamt_t0 = c4q11b
(65 missing values generated)

. *hist mmtotamt_t1, discrete
. 
. 
. gen nonmmUser_t1 = (c1b1 > 0) if _merge==3
(180 missing values generated)

. gen nonmmUser_t0=(c4q18a > 0)

. replace nonmmUser_t0=. if missing(c4q18a)
(2 real changes made, 2 to missing)

. 
. 
. gen nonmmtotnob_t1 = c1b1
(227 missing values generated)

. gen nonmmtotnob_t0 = c4q18a
(2 missing values generated)

. 
. 
. gen log_nonmmtotamt_t1 = log(c1b2+1) if !missing(c1b2)
(227 missing values generated)

. gen log_nonmmtotamt_t0 = log(c4q18b+1) if !missing(c4q18b)
(2 missing values generated)

. 
. 
. gen nonmmtotamt_t1 = c1b2
(227 missing values generated)

. gen nonmmtotamt_t0 = c4q18b
(2 missing values generated)

. 
. 
. gen save_t1 =(c3>2) if _merge==3
(180 missing values generated)

. gen save_t0 =(c4q5==1)

. replace save_t0=. if missing(c4q5)
(117 real changes made, 117 to missing)

. 
. 
. gen indebt_t1 =(c2>2) if _merge==3
(180 missing values generated)

. gen indebt_t0 =(c5q1>2)

. replace indebt_t0=. if missing(c5q1)
(2 real changes made, 2 to missing)

. 
. *tab districtID, gen(districtID)
. egen locfes = group(loccode)

. *tab locfes, gen(locfes)
. 
. save "$dta_loc_repl/02_final/Customer_+_Mktcensus_+_Interventions.dta", repl
> ace  //good? yes
file
    /Users/yazenkashlan/Library/CloudStorage/OneDrive-Personal/Documents/per
    > sonal/Berk/03_Work/Francis/Replication/data_final/02_final/Customer_+_
    > Mktcensus_+_Interventions.dta saved

. 
. 
. 
. ** -------------------------------------------------------------------------
> ----
. ** Merchants
. **************
. ***************
. use "$dta_loc_repl/00_raw_anon/Merchant_corrected.dta", clear

. 
. gen duration_min = end_time-start_time

. 
. merge m:m ge01 ge02 using `Mkt_census_xtics_int_lclzd'
(label _merge already defined)

    Result                      Number of obs
    -----------------------------------------
    Not matched                           242
        from master                         0  (_merge==1)
        from using                        242  (_merge==2)

    Matched                               748  (_merge==3)
    -----------------------------------------

. *keep if _merge ==3
. bys ge01 ge02: keep if _n==1  //only vendors + dropouts
(860 observations deleted)

. 
. **attrition stats: numbers
. tab intervention

      intervention or |
    treatment type to |
            implement |      Freq.     Percent        Cum.
----------------------+-----------------------------------
              Control |         32       24.62       24.62
    MKtMonitoring, MM |         32       24.62       49.23
PriceTransparency, PT |         31       23.85       73.08
         joint: PT+MM |         35       26.92      100.00
----------------------+-----------------------------------
                Total |        130      100.00

. gen dropouts = (_merge==2)

. tab intervention if dropouts==0

      intervention or |
    treatment type to |
            implement |      Freq.     Percent        Cum.
----------------------+-----------------------------------
              Control |         25       23.36       23.36
    MKtMonitoring, MM |         28       26.17       49.53
PriceTransparency, PT |         26       24.30       73.83
         joint: PT+MM |         28       26.17      100.00
----------------------+-----------------------------------
                Total |        107      100.00

. *get mean=% and SD=%?
. gen ins=(dropouts==0)

. tabstat ins, stat(mean sd n) by(intervention) // Table B.5

Summary for variables: ins
Group variable: intervention (intervention or treatment type to implement)

    intervention |      Mean        SD         N
-----------------+------------------------------
         Control |    .78125  .4200134        32
MKtMonitoring, M |      .875  .3360108        32
PriceTransparenc |  .8387097  .3738783        31
    joint: PT+MM |        .8  .4058397        35
-----------------+------------------------------
           Total |  .8230769  .3830798       130
------------------------------------------------

. tabstat dropouts, stat(mean sd n) by(intervention)

Summary for variables: dropouts
Group variable: intervention (intervention or treatment type to implement)

    intervention |      Mean        SD         N
-----------------+------------------------------
         Control |    .21875  .4200134        32
MKtMonitoring, M |      .125  .3360108        32
PriceTransparenc |  .1612903  .3738783        31
    joint: PT+MM |        .2  .4058397        35
-----------------+------------------------------
           Total |  .1769231  .3830798       130
------------------------------------------------

. 
. **define treatment indicators
. tab intervention

      intervention or |
    treatment type to |
            implement |      Freq.     Percent        Cum.
----------------------+-----------------------------------
              Control |         32       24.62       24.62
    MKtMonitoring, MM |         32       24.62       49.23
PriceTransparency, PT |         31       23.85       73.08
         joint: PT+MM |         35       26.92      100.00
----------------------+-----------------------------------
                Total |        130      100.00

. gen trtment = (intervention != "Control")

. 
. gen trtment_mm =.
(130 missing values generated)

. replace trtment_mm=1 if (intervention == "MKtMonitoring, MM")
(32 real changes made)

. replace trtment_mm=0 if (intervention == "Control")
(32 real changes made)

. 
. gen trtment_pt=.
(130 missing values generated)

. replace trtment_pt=1 if (intervention == "PriceTransparency, PT")
(31 real changes made)

. replace trtment_pt=0 if (intervention == "Control")
(32 real changes made)

. 
. gen trtment_mmpt=.
(130 missing values generated)

. replace trtment_mmpt=1 if (intervention == "joint: PT+MM")
(35 real changes made)

. replace trtment_mmpt=0 if (intervention == "Control")
(32 real changes made)

. 
. gen trt=0

. replace trt=1 if intervention=="PriceTransparency, PT"
(31 real changes made)

. replace trt=2 if intervention=="MKtMonitoring, MM"
(32 real changes made)

. replace trt=3 if intervention=="joint: PT+MM"
(35 real changes made)

. 
. *Attrition - Test for Significance by Treatment Program
. gen trt_pool = (trt !=0)

. sum dropouts if trt_pool==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    dropouts |         32      .21875    .4200134          0          1

. reg dropouts trt_pool, r

Linear regression                               Number of obs     =        130
                                                F(1, 128)         =       0.45
                                                Prob > F          =     0.5035
                                                R-squared         =     0.0039
                                                Root MSE          =     .38382

------------------------------------------------------------------------------
             |               Robust
    dropouts | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    trt_pool |  -.0554847    .082703    -0.67   0.503    -.2191266    .1081573
       _cons |     .21875    .073648     2.97   0.004     .0730249    .3644751
------------------------------------------------------------------------------

. reg dropouts i.trt, r

Linear regression                               Number of obs     =        130
                                                F(3, 126)         =       0.40
                                                Prob > F          =     0.7502
                                                R-squared         =     0.0089
                                                Root MSE          =     .38588

------------------------------------------------------------------------------
             |               Robust
    dropouts | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         trt |
          1  |  -.0574597   .1000619    -0.57   0.567    -.2554792    .1405599
          2  |    -.09375    .095061    -0.99   0.326    -.2818729    .0943729
          3  |    -.01875    .101127    -0.19   0.853    -.2188774    .1813774
             |
       _cons |     .21875   .0742302     2.95   0.004     .0718507    .3656493
------------------------------------------------------------------------------

. 
. 
. tab date_of_interview

    Date of |
  Interview |      Freq.     Percent        Cum.
------------+-----------------------------------
    2052020 |          4        3.74        3.74
    4052020 |          1        0.93        4.67
    6052020 |          9        8.41       13.08
    9052020 |          1        0.93       14.02
   1.01e+07 |          2        1.87       15.89
   1.11e+07 |          3        2.80       18.69
   1.41e+07 |          6        5.61       24.30
   1.51e+07 |          1        0.93       25.23
   2.20e+07 |          4        3.74       28.97
   2.30e+07 |         32       29.91       58.88
   2.40e+07 |         19       17.76       76.64
   2.50e+07 |          9        8.41       85.05
   2.70e+07 |          7        6.54       91.59
   2.80e+07 |          6        5.61       97.20
   2.90e+07 |          1        0.93       98.13
   3.00e+07 |          2        1.87      100.00
------------+-----------------------------------
      Total |        107      100.00

. tab date_of_interview, missing

    Date of |
  Interview |      Freq.     Percent        Cum.
------------+-----------------------------------
    2052020 |          4        3.08        3.08
    4052020 |          1        0.77        3.85
    6052020 |          9        6.92       10.77
    9052020 |          1        0.77       11.54
   1.01e+07 |          2        1.54       13.08
   1.11e+07 |          3        2.31       15.38
   1.41e+07 |          6        4.62       20.00
   1.51e+07 |          1        0.77       20.77
   2.20e+07 |          4        3.08       23.85
   2.30e+07 |         32       24.62       48.46
   2.40e+07 |         19       14.62       63.08
   2.50e+07 |          9        6.92       70.00
   2.70e+07 |          7        5.38       75.38
   2.80e+07 |          6        4.62       80.00
   2.90e+07 |          1        0.77       80.77
   3.00e+07 |          2        1.54       82.31
          . |         23       17.69      100.00
------------+-----------------------------------
      Total |        130      100.00

. 
. 
. *get measurements?
. ***momo sales? I
. gen mmtotamt_cust_t1 = v1a2
(23 missing values generated)

. gen mmtotamt_cust_t0 = m2q4b
(1 missing value generated)

. gen log_mmtotamt_cust_t1 = ln(v1a2)
(25 missing values generated)

. gen log_mmtotamt_cust_t0=ln(m2q4b)
(5 missing values generated)

. 
. ***non-momo sales? II
. gen nonmmtotamt_cust_t1 = v1b2
(23 missing values generated)

. gen nonmmtotamt_cust_t0 = dailyTotMoney_nonM
(28 missing values generated)

. gen log_nonmmtotamt_cust_t1 = ln(v1b2)
(26 missing values generated)

. gen log_nonmmtotamt_cust_t0=ln(dailyTotMoney_nonM)
(29 missing values generated)

. 
. 
. **Total sales-combined momo+nonmomo? III
. gen totamt_cust_t1 = mmtotamt_cust_t1+nonmmtotamt_cust_t1
(23 missing values generated)

. gen totamt_cust_t0 = mmtotamt_cust_t0+nonmmtotamt_cust_t0
(28 missing values generated)

. gen log_totamt_cust_t1 = ln(totamt_cust_t1)
(24 missing values generated)

. gen log_totamt_cust_t0 = ln(totamt_cust_t0)
(28 missing values generated)

. 
. **exits? IV
. gen bus_exit = dropouts

. 
. gen migrateDesire= (m5q1==1)

. gen migratein1yr = (m5q3 <3)

. gen migratepermanent = (m5q4 ==2)

. 
. 
. save "$dta_loc_repl/02_final/Merchants_+_Mktcensus_+_Interventions.dta", rep
> lace  //good? yes
file
    /Users/yazenkashlan/Library/CloudStorage/OneDrive-Personal/Documents/per
    > sonal/Berk/03_Work/Francis/Replication/data_final/02_final/Merchants_+
    > _Mktcensus_+_Interventions.dta saved

. 
end of do-file

. do "$do_loc/_basel2-mkt_ai"                     // generate mkt_aiVendorBett
> er

. /*
> Generate mkt_aiVendorBetter
> Input: 
>         - Mkt_fieldData_census
> Output:
>         - mkt_aiVendorBetter
> */
. 
. **Xavi - why spillovers?
. *either (i) communication (v*-v, c*-c)? or (ii) shopping around (c*-v)?
. *dependence on degree of AI?
. 
. 
. use "$dta_loc_repl/01_intermediate/Mkt_fieldData_census", clear

. 
. **Asymmetric Tnformation Test**
. bys ge02: egen mkt_m_corrects2 = mean(m_corrects)
(106 missing values generated)

. bys ge02: egen mkt_c_corrects2 = mean(c_corrects)

. 
. bys ge02: egen mkt_c_fracAnyEduc = mean(cEducAny)

. bys ge02: egen mkt_c_avgEducLevel = mean(cEduc)

. gen primandlesssEduc = (cEduc<=2) if !missing(cEduc)
(58 missing values generated)

. bys ge02: egen mkt_c_fracprimandlesssEduc = mean(primandlesssEduc)

. 
. bys ge02: keep if _n==1
(1,917 observations deleted)

. bys ge02: gen mkt_aigap = mkt_m_corrects2-mkt_c_corrects2
(7 missing values generated)

. bys ge02: gen mkt_aiVendorBetter = (mkt_m_corrects2>mkt_c_corrects2) if !mis
> sing(mkt_aigap)
(7 missing values generated)

. 
. bys ge02: gen x=_N

. rename locality_name locality_nameBase

. keep mkt_aigap mkt_aiVendorBetter mkt_c_corrects2 mkt_m_corrects2 ///
>          mkt_c_fracAnyEduc mkt_c_avgEducLevel mkt_c_fracprimandlesssEduc ///
>          text_ge02 locality_nameBase x // loccode ln

. 
. // drop if text_ge02 == .
. 
. saveold "$dta_loc_repl/01_intermediate/mkt_aiVendorBetter.dta", replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in
      older Stata formats)
file
    /Users/yazenkashlan/Library/CloudStorage/OneDrive-Personal/Documents/per
    > sonal/Berk/03_Work/Francis/Replication/data_final/01_intermediate/mkt_
    > aiVendorBetter.dta saved

. 
end of do-file

. do "$do_loc/_baselother-FinalAuditData" // generate ofdrate_mktAudit_endline

. /*
> Vendors/Customers: estimate locality-level fraud rates(objective), 
> then combine with customers estimates(subjective)
> 
> Source: 
> Input:
>         - analyzed_EndlineAuditData
> Output:
>         - ofdrate_mktAudit_endline
> */
. 
. use "$dta_loc_repl/00_raw_anon/analyzed_EndlineAuditData.dta", clear

. keep if _merge==3 //get rep vendors
(771 observations deleted)

. bys ge01 ge02: egen obj_fd = mean(fd) //continuous measure
(240 missing values generated)

. replace obj_fd = obj_fd*100 
(792 real changes made)

. bys ge01 ge02: egen obj_fdamt = mean(fdamt) //continuous
(240 missing values generated)

. pwcorr obj_fd obj_fdamt, sig

             |   obj_fd obj_fd~t
-------------+------------------
      obj_fd |   1.0000 
             |
             |
   obj_fdamt |   0.9313   1.0000 
             |   0.0000
             |

. 
. sort ge01 ge02

. // br ge0* fd fdamt fYes_T fAmt_T sv_fAmt_T
. bys ge01 ge02: keep if _n==1
(1,419 observations deleted)

. hist obj_fd, frac
(bin=10, start=0, width=10)

. hist obj_fdamt, frac
(bin=10, start=0, width=.45)

. sum obj_fd obj_fdamt, d

                           obj_fd
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                 109
25%            0              0       Sum of wgt.         109

50%     14.28572                      Mean           17.55497
                        Largest       Std. dev.      20.48024
75%     33.33334       57.14286
90%           50             60       Variance       419.4401
95%           50            100       Skewness       1.538613
99%          100            100       Kurtosis        6.03921

                          obj_fdamt
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                 109
25%            0              0       Sum of wgt.         109

50%     .1428571                      Mean           .4511249
                        Largest       Std. dev.      .7088485
75%           .8       1.833333
90%          1.5          1.875       Variance       .5024662
95%         1.75       3.333333       Skewness        2.76377
99%     3.333333            4.5       Kurtosis       13.45957

. gen fdH0 = (obj_fd>0) if !missing(obj_fd) //binary measure (above 0%)
(20 missing values generated)

. gen fdH1 = (obj_fd>14.28) if !missing(obj_fd) //binary (above median=14.28%)
(20 missing values generated)

. gen fdamtH0 = (obj_fdamt>0) if !missing(obj_fdamt)
(20 missing values generated)

. gen fdamtH1 = (obj_fdamt>0.142) if !missing(obj_fdamt)
(20 missing values generated)

. keep ge01 ge02 ge03 obj_fd obj_fdamt fdH* fdamtH*

. sort ge01 ge02 ge03

. 
. saveold "$dta_loc_repl/01_intermediate/ofdrate_mktAudit_endline.dta", replac
> e
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in
      older Stata formats)
file
    /Users/yazenkashlan/Library/CloudStorage/OneDrive-Personal/Documents/per
    > sonal/Berk/03_Work/Francis/Replication/data_final/01_intermediate/ofdr
    > ate_mktAudit_endline.dta saved

. 
end of do-file

. do "$do_loc/_followups-organized_surveys"

. /*
> Title: followup surveys [Managers + controlVendos], JPE Revision
> 
> Input:
>         - _project/_xREPUTATION/_submission/JPE/surveys-followups/
>                 - organized_surveys_cVENDORS_xlsx.xlsx
>                 - organized_surveys_MANAGERS.xlsx
> Output:
>         Data:
>         - _project/_xREPUTATION/_submission/JPE/surveys-followups/revision_r
> esults/
>                 - vendors_maindata_p
>                 - vendors_maindata_q
>                 - vendors_maindata_elas
>                 - managers_maindata_p
>                 - managers_maindata_q
>         Graphs:
>                 - vendors_misconduct_hypothesis_graph.gph
>                 - vendors_inadequateCampaigns_hypotheses_graph
>                 - vendors_beliefs_prices_graph.eps
>                 - vendors_pForecast.gph
>                 - vendors_qForecast.gph
>                 - vendors_pXqForecast.eps
>                 - vendors_elasForecast.eps
>                 - managersXvendors_misconduct_hypothesis_graph.eps
>                 - managersXvendors_inadequateCampaigns_hypotheses_graph.eps
>                 - managers_pForecast.gph
>                 - managers_qForecast.gph
>                 - managers_pXqForecast.eps              
>                 
> */
. 
. version
version 14.0

. 
. ********************
. *Vendors Perspective
. ********************
. use "$dta_loc_repl/00_raw_anon/organized_surveys_cVENDORS_xlsx", clear

. 
. ** Figure B.11 -------------------------------------------------------------
> ----
. *1. Why pre-experment overcharging? we advance a number of hypothesis 
. *[based on focus market group discussions + baseline data descriptives + rev
> iewer suggestions], top 1-4 (out of 9 hyp) are:
. *(i) uninformed customers [formally evaluated above - 3 highs]
. *(ii=) low vendor commissions so, overcharging is a short-run incentive to r
> ip off consumers
. *(ii=) inadequate campaign in rural areas by provider MTN ...[formally evalu
> ated now - why descriptively? + below - sect YY: mtn why not tackle if profi
> table]
. *(ii=) possibly misgueded firm beliefs about pricing...[formally evaluated b
> elow - sect YY: v why if not "too" profitable]
. *others include: (v) limited competition [survey+data cut] + (vi) perceived 
> cost of misconduct is low [survey], including weak agency relation b/n vendo
> rs and provider (r=vii), limited consumer search aka "Ghanaians don't like c
> hange" (r=viii) and heterogeneity (r=ix)
. **these are all plausbisible, we dont have enough evidence to neither advanc
> e nor separate them**
. **yet, as shown here and above, the issue of "uninformed consumers", which i
> n itself exacebates the effects from other possible hypothesis and though no
> t the only pluasible hypothesis, is very crucial -- hence the "focus of expe
> riment"
. 
. *summarizing approach:
. **we recoded the rankings to get a rank of 1 achieve the highest score
. **for each hypothesis (column), cal. total ranking (=scores) across all resp
> ondents, then plot - scores for each hypothesis*
. 
. gr bar (sum) QL1_1-QL1_9, //top 1-4: here: uninformed consumers>>low commiss
> ions>limited campaings>misguided vendor beliefs>competition>low perceived co
> st

. foreach var of varlist QL1_1 QL1_2 QL1_3 QL1_4 QL1_5 QL1_6{
  2.         recode `var' 1=9 2=8 3=7 4=6 5=5 9=1 8=2 7=3 6=4, gen(rec_`var')
  3. }
(52 differences between QL1_1 and rec_QL1_1)
(58 differences between QL1_2 and rec_QL1_2)
(44 differences between QL1_3 and rec_QL1_3)
(58 differences between QL1_4 and rec_QL1_4)
(38 differences between QL1_5 and rec_QL1_5)
(50 differences between QL1_6 and rec_QL1_6)

. *
. graph hbar (sum) rec_QL1_1 - rec_QL1_6, nofill asyvars ///
>  blabel(group, position(inside) format(%4.0f) box fcolor(white) lcolor(white
> )) ytitle("Why Misconduct: Rank scores for possible hypotheses", size(vsmall
> )) blabel(bar) ///
>  legend(pos(4) row(6) stack label(1 "Inadequate campaigns by provider MTN in
>  rural areas") label(2 "Poorly informed consumers - prices and redress chann
> els") label(3 "Possibly misguided vendor beliefs about pricing") label(4 "Lo
> w vendor commissions as short-run incentive") label(5 "Limited competition -
>  vendor options and alternatives") label(6 "Low perceived cost of misconduct
> ") size(tiny)) note(" " "{bf:Vendors views}, [N=58 Vendors]")

. *gr export "$dta_loc/_project/_xREPUTATION/_submission/JPE/surveys-followups
> /revision_results/vendors_misconduct_hypothesis_graph.eps", replace
. gr save "$output_loc/followup/vendors_misconduct_hypothesis_graph.gph", repl
> ace
file /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_cop
> y/output/followup/vendors_misconduct_hypothesis_graph.gph saved

. 
. 
. ** Figure C.2 --------------------------------------------------------------
> ----
. *2a...then why MTN hasnt - survey evidence?
. gr bar QL2_1 QL2_2 QL2_3 QL2_4 //top 1 (out of 4 reasons): Too costly (+ lac
> k of workable sol that can scale in rural areas[evaluated in sect YY])

. foreach var of varlist QL2_1 QL2_2 QL2_3 QL2_4{
  2.         recode `var' 1=4 2=3 4=1 3=2, gen(rec_`var')
  3. }
(57 differences between QL2_1 and rec_QL2_1)
(58 differences between QL2_2 and rec_QL2_2)
(58 differences between QL2_3 and rec_QL2_3)
(54 differences between QL2_4 and rec_QL2_4)

. *
. graph hbar (sum) rec_QL2_1 - rec_QL2_4, nofill asyvars ///
>  blabel(group, position(inside) format(%4.0f) box fcolor(white) lcolor(white
> )) ytitle("Why Inadequate Campaigns: Rank scores for reasons", size(vsmall))
>  blabel(bar) ///
>  legend(pos(4) row(6) stack label(1 "Too costly to deliver rural anti-overch
> arging campaigns") label(2 "MTN is not aware of vendors overcharging in rura
> l areas") label(3 "Too many vendors  to come up with workable solutions at s
> cale") label(4 "MTN do not care") size(tiny)) note(" " "{bf:Vendors views}, 
> [N=58 Vendors]")

. *gr export "$dta_loc/_project/_xREPUTATION/_submission/JPE/surveys-followups
> /revision_results/vendors_inadequateCampaigns_hypotheses_graph.eps", replace
. gr save "$output_loc/followup/vendors_inadequateCampaigns_hypotheses_graph.g
> ph", replace
file /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_cop
> y/output/followup/vendors_inadequateCampaigns_hypotheses_graph.gph saved

. 
. 
. ** Figure C.1 --------------------------------------------------------------
> ----
. **2b: possibly misguided firm beliefs about pricing...[formal evaluation]
. **(a)cVendors subjective beliefs about profit-max prices: QP1 QP2 QP3
. tab QP1, miss //most vendors 59% perceive (higher_p, lowQ >> lower_p, highQ)
> , n=58

        QP1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         34       58.62       58.62
          2 |         24       41.38      100.00
------------+-----------------------------------
      Total |         58      100.00

. gen higher_p=(QP1=="1")

. gen lower_p=(QP1=="2")

. tab higher_p 

   higher_p |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         24       41.38       41.38
          1 |         34       58.62      100.00
------------+-----------------------------------
      Total |         58      100.00

. tab lower_p

    lower_p |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         34       58.62       58.62
          1 |         24       41.38      100.00
------------+-----------------------------------
      Total |         58      100.00

. sum higher_p lower_p

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    higher_p |         58    .5862069    .4968138          0          1
     lower_p |         58    .4137931    .4968138          0          1

. ttesti 58 0.59 0.49 58 0.41 0.49 //pval=0.0503

Two-sample t test with equal variances
------------------------------------------------------------------------------
         |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       x |      58         .59    .0643402         .49    .4611611    .7188389
       y |      58         .41    .0643402         .49    .2811611    .5388389
---------+--------------------------------------------------------------------
Combined |     116          .5     .046068    .4961679    .4087481    .5912519
---------+--------------------------------------------------------------------
    diff |                 .18    .0909907               -.0002519    .3602519
------------------------------------------------------------------------------
    diff = mean(x) - mean(y)                                      t =   1.9782
H0: diff = 0                                     Degrees of freedom =      114

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9748         Pr(|T| > |t|) = 0.0503          Pr(T > t) = 0.0252

. ttest higher_p == lower_p, unpaired //pval=0.0642

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
higher_p |      58    .5862069    .0652348    .4968138    .4555764    .7168374
 lower_p |      58    .4137931    .0652348    .4968138    .2831626    .5444236
---------+--------------------------------------------------------------------
Combined |     116          .5    .0466252    .5021692    .4076444    .5923556
---------+--------------------------------------------------------------------
    diff |            .1724138     .092256               -.0103446    .3551722
------------------------------------------------------------------------------
    diff = mean(higher_p) - mean(lower_p)                         t =   1.8689
H0: diff = 0                                     Degrees of freedom =      114

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9679         Pr(|T| > |t|) = 0.0642          Pr(T > t) = 0.0321

. 
. graph hbar higher_p lower_p, bar(1, color(black)) bar(2, color(gs8)) nofill 
> asyvars ///
>  blabel(group, position(inside) format(%4.2f) box fcolor(white) lcolor(white
> )) ytitle("Beliefs about Profit-Maximizing Prices: Share indicating higher v
> s lower price", size(small)) blabel(bar) ///
>  legend(pos(7) row(1) stack label(1 "Higher price") label(2 "Lower price"))

. gr export "$output_loc/followup/vendors_beliefs_prices_graph.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/followup/vendors_beliefs_prices_graph.eps saved as EPS
    format

. 
. 
. 
. ** Figure C.1 --------------------------------------------------------------
> ----
. **(b)cVendors predictions of mkt-level te(p,d) of information interventions
. ***************************************************************************
. *QT1_1 (0/1 overcharge?) 
. *QT1_2 (amt overcharge?)
. *QT1_3 (amt consumer's usage, weekly?)
. *QT1_4 (amt vendor's sales, daily?)
. *To ease the presentation / exposition: I focus: 0/1 overcgaring (p) and amt
>  consumer demand (q)
. *results extend easily to amt overcharging and sales revenues and available 
> upon request...
. 
. destring QT1_1, generate(QT1_1num) ignore(%) //trt 1 0/1
QT1_1: character % removed; QT1_1num generated as byte

. destring QT1_2, generate(QT1_2num) ignore(%) //trt 1 amt GHS = 0/1 SAME PRED
> ICTIONS, OK
QT1_2: character % removed; QT1_2num generated as byte

. destring QT1_3, generate(QT1_3num) ignore(%)
QT1_3: character % removed; QT1_3num generated as byte

. 
. replace QT2_1 = "-75%" if QT2_1=="-.75" //data entry error//
(1 real change made)

. replace QT2_3 = "-75%" if QT2_1=="-.75" //data entry error//
(0 real changes made)

. destring QT2_1, generate(QT2_1num) ignore(%) //trt 2 0/1
QT2_1: character % removed; QT2_1num generated as byte

. destring QT2_2, generate(QT2_2num) ignore(%) //trt 2 amt GHS = 0/1 SAME PRED
> ICTIONS, OK
QT2_2: character % removed; QT2_2num generated as double

. destring QT2_3, generate(QT2_3num) ignore(%)
QT2_3: character % removed; QT2_3num generated as byte

. 
. replace QT3_1 = "-75%" if QT3_1=="-.75" //data entry error//
(8 real changes made)

. replace QT3_3 = "-75%" if QT3_1=="-.75" //data entry error//
(0 real changes made)

. destring QT3_1, generate(QT3_1num) ignore(%) //trt 3 0/1
QT3_1: character % removed; QT3_1num generated as byte

. destring QT3_2, generate(QT3_2num) ignore(%) //trt 3 amt GHS = 0/1 SAME PRED
> ICTIONS, OK
QT3_2: character % removed; QT3_2num generated as double

. destring QT3_3, generate(QT3_3num) ignore(%)
QT3_3: character % removed; QT3_3num generated as byte

. 
. *??elasticity at individual level???*
. gen elas1_num =abs(QT1_3num/QT1_1num)
(21 missing values generated)

. gen elas2_num =abs(QT2_3num/QT2_1num)

. gen elas3_num =abs(QT3_3num/QT3_1num)

. 
. 
. ******************
. *p* in long format
. ******************
. preserve

.         keep QT1_1num QT2_1num QT3_1num

.         gen id = _n

.         reshape long QT, i(id) j(trt) string
(j = 1_1num 2_1num 3_1num)

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations               58   ->   174         
Number of variables                   4   ->   3           
j variable (3 values)                     ->   trt
xij variables:
             QT1_1num QT2_1num QT3_1num   ->   QT
-----------------------------------------------------------------------------

.         replace trt="1" if trt=="1_1num"
(58 real changes made)

.         replace trt="2" if trt=="2_1num"
(58 real changes made)

.         replace trt="3" if trt=="3_1num"
(58 real changes made)

.         destring trt, replace
trt: all characters numeric; replaced as byte

. 
.         saveold "$dta_loc_repl/01_intermediate/vendors_maindata_p", replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in
      older Stata formats)
file
    /Users/yazenkashlan/Library/CloudStorage/OneDrive-Personal/Documents/per
    > sonal/Berk/03_Work/Francis/Replication/data_final/01_intermediate/vend
    > ors_maindata_p.dta saved

. 
.         use "$dta_loc_repl/01_intermediate/vendors_maindata_p", clear

.         statsby, by(trt): ci QT
(running ci on estimation sample)

      Command: ci QT
           ub: r(ub)
           lb: r(lb)
           se: r(se)
         mean: r(mean)
            N: r(N)
           By: trt

Statsby groups:
...

.         label define lbl 1 "Price Transparency" 2 "Monitor & Report" 3 "Join
> t: PT + MR"

.         label value trt lbl

.         levelsof trt, local(levels)
1 2 3

.         twoway bar mean trt, barw(0.8) bfcolor(green*0.2) ylab(0(-10)-70) yl
> ine(0, lp(dash)) || rcap lb ub trt, xlabel(`levels', valuelabel angle(45) la
> bsize(small)) scheme(s1color) legend(off) ytitle("Forecasted treatment effec
> t (%)", size(med)) xtitle("Treatment program") note(" " "{bf:Prices:} Miscon
> duct: 0-1, [N=58 Vendors]" "{bf:Observed Treatment Effects:}" "PT= -62%, MR=
>  -73%, Joint= -72%; [Pooled= -72%]", position(7))
(note:  named style med not found in class gsize, default attributes used)

.         gr save "$output_loc/followup/vendors_pForecast.gph", replace
file /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_cop
> y/output/followup/vendors_pForecast.gph saved

. restore

. 
. 
. ******************
. *q* in long format
. ******************
. preserve

.         keep QT1_3num QT2_3num QT3_3num

.         gen id = _n

.         reshape long QT, i(id) j(trt) string
(j = 1_3num 2_3num 3_3num)

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations               58   ->   174         
Number of variables                   4   ->   3           
j variable (3 values)                     ->   trt
xij variables:
             QT1_3num QT2_3num QT3_3num   ->   QT
-----------------------------------------------------------------------------

.         replace trt="1" if trt=="1_3num"
(58 real changes made)

.         replace trt="2" if trt=="2_3num"
(58 real changes made)

.         replace trt="3" if trt=="3_3num"
(58 real changes made)

.         destring trt, replace
trt: all characters numeric; replaced as byte

. 
.         saveold "$dta_loc_repl/01_intermediate/vendors_maindata_q", replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in
      older Stata formats)
file
    /Users/yazenkashlan/Library/CloudStorage/OneDrive-Personal/Documents/per
    > sonal/Berk/03_Work/Francis/Replication/data_final/01_intermediate/vend
    > ors_maindata_q.dta saved

. 
.         statsby, by(trt): ci QT
(running ci on estimation sample)

      Command: ci QT
           ub: r(ub)
           lb: r(lb)
           se: r(se)
         mean: r(mean)
            N: r(N)
           By: trt

Statsby groups:
...

.         label define lbl 1 "Price Transparency" 2 "Monitor & Report" 3 "Join
> t: PT + MR"

.         label value trt lbl

.         levelsof trt, local(levels)
1 2 3

. 
.         twoway bar mean trt, barw(0.8) bfcolor(green*0.2) ylab(-2(2)10) ylin
> e(0, lp(dash)) || rcap lb ub trt, xlabel(`levels', valuelabel angle(45) labs
> ize(small)) scheme(s1color) legend(off) ytitle("Forecasted treatment effect 
> (%)", size(med)) xtitle("Treatment program") note(" " "{bf:Quantities:} Cons
> umer Transactions (weekly), [N=58 Vendors]" "{bf:Observed Treatment Effects:
> }" "PT= +26%, MR= +58%, Joint= +54%; [Pooled= +45%]", position(7))
(note:  named style med not found in class gsize, default attributes used)

.         gr save "$output_loc/followup/vendors_qForecast.gph", replace
file /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_cop
> y/output/followup/vendors_qForecast.gph saved

. restore

. 
. *vendors: (p,q) combined....
. ****************************
. gr combine "$output_loc/followup/vendors_pForecast.gph" ///
>                         "$output_loc/followup/vendors_qForecast.gph"
(note:  named style med not found in class gsize, default attributes used)
(note:  named style med not found in class gsize, default attributes used)

. gr export "$output_loc/followup/vendors_pXqForecast.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/followup/vendors_pXqForecast.eps saved as EPS format

. *p details
. tab QT1_1num //36% vendors = 0 effect

      QT1_1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        -50 |          2        3.45        3.45
        -25 |         35       60.34       63.79
          0 |         21       36.21      100.00
------------+-----------------------------------
      Total |         58      100.00

. tab QT2_1num //0% vendors = 0 effect

      QT2_1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        -75 |          2        3.45        3.45
        -50 |         23       39.66       43.10
        -25 |         33       56.90      100.00
------------+-----------------------------------
      Total |         58      100.00

. tab QT3_1num //0% vendors = 0 effect

      QT3_1 |      Freq.     Percent        Cum.
------------+-----------------------------------
       -100 |          2        3.45        3.45
        -75 |          9       15.52       18.97
        -50 |         46       79.31       98.28
        -25 |          1        1.72      100.00
------------+-----------------------------------
      Total |         58      100.00

. *q details
. tab QT1_3num //95% vendors = 0 effect

      QT1_3 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         55       94.83       94.83
         25 |          3        5.17      100.00
------------+-----------------------------------
      Total |         58      100.00

. tab QT2_3num //91% vendors = 0 effect

      QT2_3 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         53       91.38       91.38
         25 |          5        8.62      100.00
------------+-----------------------------------
      Total |         58      100.00

. tab QT3_3num //85% vendors = 0 effect

      QT3_3 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         49       84.48       84.48
         25 |          9       15.52      100.00
------------+-----------------------------------
      Total |         58      100.00

. *overall -- incorrect forecasts; but correct in direction + trends;  predict
>  large impacts on p (assuring) but doesn't rise to observed very large effec
> t sizes; predict very small to no impacts on q (incorrect and very far from 
> observed) -> incorrectly predicting q'(p)
. *hence -- make's sense why they may subjectively believe overcharging (higer
>  p) is better (b/c they can't predict q'(p) well, an ingredient in setting p
> rices or markups)
. 
. 
. **ELASTICITY**
. ******************
. *q* in long format
. ******************
. preserve

.         keep elas1_num elas2_num elas3_num

.         gen id = _n

.         reshape long elas, i(id) j(trt) string
(j = 1_num 2_num 3_num)

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations               58   ->   174         
Number of variables                   4   ->   3           
j variable (3 values)                     ->   trt
xij variables:
          elas1_num elas2_num elas3_num   ->   elas
-----------------------------------------------------------------------------

.         replace trt="1" if trt=="1_num"
(58 real changes made)

.         replace trt="2" if trt=="2_num"
(58 real changes made)

.         replace trt="3" if trt=="3_num"
(58 real changes made)

.         destring trt, replace
trt: all characters numeric; replaced as byte

.         sum elas

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
        elas |        153    .0479303    .1473737          0          1

. 
.         saveold "$dta_loc_repl/01_intermediate/vendors_maindata_elas", repla
> ce
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in
      older Stata formats)
file
    /Users/yazenkashlan/Library/CloudStorage/OneDrive-Personal/Documents/per
    > sonal/Berk/03_Work/Francis/Replication/data_final/01_intermediate/vend
    > ors_maindata_elas.dta saved

. 
.         statsby, by(trt): ci elas
(running ci on estimation sample)

      Command: ci elas
           ub: r(ub)
           lb: r(lb)
           se: r(se)
         mean: r(mean)
            N: r(N)
           By: trt

Statsby groups:
...

.         label define lbl 1 "Price Transparency" 2 "Monitor & Report" 3 "Join
> t: PT + MR"

.         label value trt lbl

.         levelsof trt, local(levels)
1 2 3

. 
.         twoway bar mean trt, barw(0.8) bfcolor(green*0.2)  yline(0, lp(dash)
> ) || rcap lb ub trt, xlabel(`levels', valuelabel angle(45) labsize(small)) s
> cheme(s1color) legend(off) ytitle("Forecasted elasticity", size(med)) xtitle
> ("Treatment program") note(" " "{bf:Elasticity:} % Quantity / % Price, [N=58
>  Vendors]" "{bf:Observed Elasticity:}" "PT= 0.65, MR= 1.45, Joint= 1.35; [Po
> oled= 1.13]", position(7))
(note:  named style med not found in class gsize, default attributes used)

.         gr export "$output_loc/followup/vendors_elasForecast.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/followup/vendors_elasForecast.eps saved as EPS format

.         gr save "$output_loc/followup/vendors_elasForecast.gph", replace
file /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_cop
> y/output/followup/vendors_elasForecast.gph saved

. restore

. 
. 
. 
. 
. 
. **********************
. *Managers Perspective
. **********************
. clear all

. use "$dta_loc_repl/00_raw_anon/organized_surveys_MANAGERS", clear

. 
. ** Figure B.11 -------------------------------------------------------------
> ----
. *1. Why pre-experment overcharging? we advance a number of hypothesis 
. gr bar (sum) QL1_1-QL1_9 //here: top 1-4: uninformed consumers>misguided ven
> dor beliefs>limited campaings>low commissions>competition>low perceived cost

. *key 1: vendors top 4 ranking preserved (e.g., uninformed consumers #1), exc
> ept that misguided beliefs now ranked much higher (#2), all else equal
. *key 2: overall rankings invariate to different aggragation approaches: medi
> an scores, mean scores
. foreach var of varlist QL1_1 QL1_2 QL1_3 QL1_4 QL1_5 QL1_6{
  2.         recode `var' 1=9 2=8 3=7 4=6 5=5 9=1 8=2 7=3 6=4, gen(rec_`var')
  3. }
(26 differences between QL1_1 and rec_QL1_1)
(28 differences between QL1_2 and rec_QL1_2)
(26 differences between QL1_3 and rec_QL1_3)
(24 differences between QL1_4 and rec_QL1_4)
(23 differences between QL1_5 and rec_QL1_5)
(21 differences between QL1_6 and rec_QL1_6)

. *
. graph hbar (sum) rec_QL1_1 - rec_QL1_6, nofill asyvars ///
>  blabel(group, position(inside) format(%4.0f) box fcolor(white) lcolor(white
> )) ytitle("Why Misconduct: Rank scores for possible hypotheses", size(vsmall
> )) blabel(bar) ///
>  legend(pos(4) row(6) stack label(1 "Inadequate campaigns by provider MTN in
>  rural areas") label(2 "Poorly informed consumers - prices and redress chann
> els") label(3 "Possibly misguided vendor beliefs about pricing") label(4 "Lo
> w vendor commissions as short-run incentive") label(5 "Limited competition -
>  vendor options and alternatives") label(6 "Low perceived cost of misconduct
> ") size(tiny)) note(" " "{bf:Managers views}, [N=29 Managers]")

. gr export "$output_loc/followup/managers_misconduct_hypothesis_graph.eps", r
> eplace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/followup/managers_misconduct_hypothesis_graph.eps saved as
    EPS format

. gr save "$output_loc/followup/managers_misconduct_hypothesis_graph.gph", rep
> lace
file /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_cop
> y/output/followup/managers_misconduct_hypothesis_graph.gph saved

. 
. *combine [managers + vendors]: why pre-experiment....
. gr combine "$output_loc/followup/managers_misconduct_hypothesis_graph.gph" /
> //
>                         "$output_loc/followup/vendors_misconduct_hypothesis_
> graph.gph"

. gr export "$output_loc/followup/managersXvendors_misconduct_hypothesis_graph
> .eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/followup/managersXvendors_misconduct_hypothesis_graph.eps
    saved as EPS format

. 
. 
. ** Figure C.2 --------------------------------------------------------------
> ----
. *2a...then why MTN hasnt - survey evidence?
. gr bar QL2_1 QL2_2 QL2_3 QL2_4 //top 1: Too costly (+ lack of workable sol t
> hat can scale in rural areas[evaluated in sect YY])

. *key 1: rankings fully preserved, same across vendors and mamagers
. foreach var of varlist QL2_1 QL2_2 QL2_3 QL2_4{
  2.         recode `var' 1=4 2=3 4=1 3=2, gen(rec_`var')
  3. }
(28 differences between QL2_1 and rec_QL2_1)
(29 differences between QL2_2 and rec_QL2_2)
(27 differences between QL2_3 and rec_QL2_3)
(24 differences between QL2_4 and rec_QL2_4)

. *
. graph hbar (sum) rec_QL2_1 - rec_QL2_4, nofill asyvars ///
>  blabel(group, position(inside) format(%4.0f) box fcolor(white) lcolor(white
> )) ytitle("Why Inadequate Campaigns: Rank scores for reasons", size(vsmall))
>  blabel(bar) ///
>  legend(pos(4) row(6) stack label(1 "Too costly to deliver rural anti-overch
> arging campaigns") label(2 "MTN is not aware of vendors overcharging in rura
> l areas") label(3 "Too many vendors  to come up with workable solutions at s
> cale") label(4 "MTN do not care") size(tiny)) note(" " "{bf:Managers views},
>  [N=29 Managers]")

. gr export "$output_loc/followup/managers_inadequateCampaigns_hypotheses_grap
> h.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/followup/managers_inadequateCampaigns_hypotheses_graph.eps
    saved as EPS format

. gr save "$output_loc/followup/managers_inadequateCampaigns_hypotheses_graph.
> gph", replace
file /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_cop
> y/output/followup/managers_inadequateCampaigns_hypotheses_graph.gph saved

. 
. *combine [managers + vendors]: why MTN hasnt....
. gr combine "$output_loc/followup/managers_inadequateCampaigns_hypotheses_gra
> ph.gph" ///
>                         "$output_loc/followup/vendors_inadequateCampaigns_hy
> potheses_graph.gph"

. gr export "$output_loc/followup/managersXvendors_inadequateCampaigns_hypothe
> ses_graph.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/followup/managersXvendors_inadequateCampaigns_hypotheses_gra
    > ph.eps saved as EPS format

. 
. 
. ** Figure C.2 --------------------------------------------------------------
> ----
. **2b. why MTN hasnt....Or manager's hasnt [formal evaluation]
. **(a) Managers predictions of mkt-level te(p,d) of information interventions
. ****************************************************************************
. *QT1_1 (0/1 overcharge?) 
. *QT1_2 (amt overcharge?)
. *QT1_3 (amt consumer's usage, weekly?)
. *QT1_4 (amt vendor's sales, daily?)
. *To ease the presentation / exposition: I focus: 0/1 overcgaring (p) and amt
>  consumer demand (q)
. *results extend easily to amt overcharging and sales revenues and available 
> upon request...
. 
. destring QT1_1, generate(QT1_1num) ignore(%) //trt 1
QT1_1: character % removed; QT1_1num generated as byte

. destring QT1_3, generate(QT1_3num) ignore(%)
QT1_3: character % removed; QT1_3num generated as byte

. 
. destring QT2_1, generate(QT2_1num) ignore(%) //trt 2
QT2_1: character % removed; QT2_1num generated as byte

. destring QT2_3, generate(QT2_3num) ignore(%)
QT2_3: character % removed; QT2_3num generated as byte

. 
. destring QT3_1, generate(QT3_1num) ignore(%) //trt 3
QT3_1: character % removed; QT3_1num generated as byte

. destring QT3_3, generate(QT3_3num) ignore(%)
QT3_3: character % removed; QT3_3num generated as byte

. 
. ******************
. *p* in long format
. ******************
. preserve

.         keep QT1_1num QT2_1num QT3_1num

.         gen id = _n

.         reshape long QT, i(id) j(trt) string
(j = 1_1num 2_1num 3_1num)

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations               29   ->   87          
Number of variables                   4   ->   3           
j variable (3 values)                     ->   trt
xij variables:
             QT1_1num QT2_1num QT3_1num   ->   QT
-----------------------------------------------------------------------------

.         replace trt="1" if trt=="1_1num"
(29 real changes made)

.         replace trt="2" if trt=="2_1num"
(29 real changes made)

.         replace trt="3" if trt=="3_1num"
(29 real changes made)

.         destring trt, replace
trt: all characters numeric; replaced as byte

. 
.         saveold "$dta_loc_repl/01_intermediate/managers_maindata_p", replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in
      older Stata formats)
file
    /Users/yazenkashlan/Library/CloudStorage/OneDrive-Personal/Documents/per
    > sonal/Berk/03_Work/Francis/Replication/data_final/01_intermediate/mana
    > gers_maindata_p.dta saved

. 
.         statsby, by(trt): ci QT
(running ci on estimation sample)

      Command: ci QT
           ub: r(ub)
           lb: r(lb)
           se: r(se)
         mean: r(mean)
            N: r(N)
           By: trt

Statsby groups:
...

.         label define lbl 1 "Price Transparency" 2 "Monitor & Report" 3 "Join
> t: PT + MR"

.         label value trt lbl

.         levelsof trt, local(levels)
1 2 3

.         twoway bar mean trt, barw(0.8) bfcolor(green*0.2)  yline(0, lp(dash)
> ) || rcap lb ub trt, xlabel(`levels', valuelabel angle(45) labsize(small)) s
> cheme(s1color) legend(off) ytitle("Forecasted treatment effect (%)", size(me
> d)) xtitle("Treatment program") note(" " "{bf:Prices:} Misconduct: 0-1, [N=2
> 9 Managers]" "{bf:Observed Treatment Effects:}" "PT= -62%, MR= -73, Joint= -
> 72%; [Pooled= -72%]", position(7))
(note:  named style med not found in class gsize, default attributes used)

.         gr save "$output_loc/followup/managers_pForecast.gph", replace
file /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_cop
> y/output/followup/managers_pForecast.gph saved

. restore

. 
. ******************
. *q* in long format
. ******************
. preserve

.         keep QT1_3num QT2_3num QT3_3num

.         gen id = _n

.         reshape long QT, i(id) j(trt) string
(j = 1_3num 2_3num 3_3num)

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations               29   ->   87          
Number of variables                   4   ->   3           
j variable (3 values)                     ->   trt
xij variables:
             QT1_3num QT2_3num QT3_3num   ->   QT
-----------------------------------------------------------------------------

.         replace trt="1" if trt=="1_3num"
(29 real changes made)

.         replace trt="2" if trt=="2_3num"
(29 real changes made)

.         replace trt="3" if trt=="3_3num"
(29 real changes made)

.         destring trt, replace
trt: all characters numeric; replaced as byte

. 
.         saveold "$dta_loc_repl/01_intermediate/managers_maindata_q", replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in
      older Stata formats)
file
    /Users/yazenkashlan/Library/CloudStorage/OneDrive-Personal/Documents/per
    > sonal/Berk/03_Work/Francis/Replication/data_final/01_intermediate/mana
    > gers_maindata_q.dta saved

. 
.         statsby, by(trt): ci QT
(running ci on estimation sample)

      Command: ci QT
           ub: r(ub)
           lb: r(lb)
           se: r(se)
         mean: r(mean)
            N: r(N)
           By: trt

Statsby groups:
...

.         label define lbl 1 "Price Transparency" 2 "Monitor & Report" 3 "Join
> t: PT + MR"

.         label value trt lbl

.         levelsof trt, local(levels)
1 2 3

. 
.         twoway bar mean trt, barw(0.8) bfcolor(green*0.2) yline(0, lp(dash))
>  || rcap lb ub trt, xlabel(`levels', valuelabel angle(45) labsize(small)) sc
> heme(s1color) legend(off) ytitle("Forecasted treatment effect (%)", size(med
> )) xtitle("Treatment program") note(" " "{bf:Quantities:} Consumer Transacti
> ons (weekly), [N=29 Managers]" "{bf:Observed Treatment Effects:}" "PT= +26%,
>  MR= +58%, Joint= +54%; [Pooled= +45%]", position(7))
(note:  named style med not found in class gsize, default attributes used)

.         gr save "$output_loc/followup/managers_qForecast.gph", replace
file /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_cop
> y/output/followup/managers_qForecast.gph saved

. restore

. 
. *managers: (p,q) combined....
. *****************************
. gr combine "$output_loc/followup/managers_pForecast.gph" ///
>                         "$output_loc/followup/managers_qForecast.gph"
(note:  named style med not found in class gsize, default attributes used)
(note:  named style med not found in class gsize, default attributes used)

. gr export "$output_loc/followup/managers_pXqForecast.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/followup/managers_pXqForecast.eps saved as EPS format

. *p details
. tab QT1_1num //52% managers = 0 effect

      QT1_1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        -75 |          2        6.90        6.90
        -25 |          2        6.90       13.79
          0 |         15       51.72       65.52
         25 |          2        6.90       72.41
         50 |          2        6.90       79.31
         75 |          2        6.90       86.21
        100 |          4       13.79      100.00
------------+-----------------------------------
      Total |         29      100.00

. tab QT2_1num //45% managers = 0 effect

      QT2_1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        -75 |          1        3.45        3.45
        -25 |          8       27.59       31.03
          0 |         13       44.83       75.86
         25 |          1        3.45       79.31
         50 |          2        6.90       86.21
         75 |          2        6.90       93.10
        100 |          2        6.90      100.00
------------+-----------------------------------
      Total |         29      100.00

. tab QT3_1num //38% managers = 0 effect

      QT3_1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        -50 |          4       13.79       13.79
        -25 |          4       13.79       27.59
          0 |         11       37.93       65.52
         25 |          2        6.90       72.41
         50 |          2        6.90       79.31
         75 |          3       10.34       89.66
        100 |          3       10.34      100.00
------------+-----------------------------------
      Total |         29      100.00

. *q details
. tab QT1_3num //72% managers = 0 effect

      QT1_3 |      Freq.     Percent        Cum.
------------+-----------------------------------
        -25 |          1        3.45        3.45
          0 |         21       72.41       75.86
         50 |          3       10.34       86.21
         75 |          2        6.90       93.10
        100 |          2        6.90      100.00
------------+-----------------------------------
      Total |         29      100.00

. tab QT2_3num //65% managers = 0 effect

      QT2_3 |      Freq.     Percent        Cum.
------------+-----------------------------------
        -75 |          1        3.45        3.45
        -25 |          1        3.45        6.90
          0 |         19       65.52       72.41
         25 |          2        6.90       79.31
         50 |          2        6.90       86.21
         75 |          2        6.90       93.10
        100 |          2        6.90      100.00
------------+-----------------------------------
      Total |         29      100.00

. tab QT3_3num //55% managers = 0 effect

      QT3_3 |      Freq.     Percent        Cum.
------------+-----------------------------------
        -75 |          1        3.45        3.45
          0 |         16       55.17       58.62
         25 |          6       20.69       79.31
         50 |          1        3.45       82.76
         75 |          2        6.90       89.66
        100 |          3       10.34      100.00
------------+-----------------------------------
      Total |         29      100.00

. *overall -- systematically incorrect forecasts, in some cases - direction + 
> trends are incorrect /opposite -- for both p and q; (all average forecasts d
> oesn't rise to observed te's very large effect sizes); 
. *hence -- make's sense why they haven't explored similar 2 sided information
>  interventions (though could be profitable to provider MTN), why dont know?:
>  perhaps due to lack of past evidence that these programs work. we are confi
> dence this ll open opportunity for takeup and scaleup by MTN
. 
. 
. 
. 
. 
end of do-file

. 
. 
. ** Main analysis
. do "$do_loc/_BalanceTest_stratadummies.do" // quick

. /*
> Title: Balance tests?
> 
> Input:
>         - Mkt_fieldData_sample_repMkt,dta
>         - interventionsTomake_list_local
> Output:
>         - Regressions, no table (esttab etc) yet
>         
> */
. 
. ***ADD STRATA DUMMIES R3 (JPE)***
. 
. ** Table B.3 ---------------------------------------------------------------
> ----
. **Balance Tests II (program assignments): JULY 4 2020 = APR 3 2023
. use "$dta_loc_repl/01_intermediate/Mkt_fieldData_sample_repMkt", clear

. merge m:m ge02 ge03 using "$dta_loc_repl/01_intermediate/interventionsTomake
> _list_local"

    Result                      Number of obs
    -----------------------------------------
    Not matched                           866
        from master                       866  (_merge==1)
        from using                          0  (_merge==2)

    Matched                               990  (_merge==3)
    -----------------------------------------

. 
. keep if _merge==3
(866 observations deleted)

. 
. *use interventionsTomake_list_local, clear
. gen trt0vsall = (treatment !=0)

. *br districtName regionDistrictCode_j localityName localityCode_j treatment 
> Trt* trt*
. 
. ***ADD STRATA DUMMIES R3 (JPE)***
. egen strataFE = group(ge01)

. 
. **Supply side: merchants...?
. reg mfemale i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        977
                                                F(11, 128)        =       1.45
                                                Prob > F          =     0.1587
                                                R-squared         =     0.1050
                                                Root MSE          =     .46977

                                 (Std. err. adjusted for 129 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
     mfemale | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |  -.1715641   .1798099    -0.95   0.342    -.5273488    .1842206
          3  |    .142347   .2429489     0.59   0.559     -.338369    .6230629
          4  |    .072712   .1942071     0.37   0.709    -.3115599    .4569839
          5  |    .249258   .2322199     1.07   0.285    -.2102288    .7087448
          6  |  -.0989834   .1924752    -0.51   0.608    -.4798285    .2818617
          7  |  -.0050321   .2329179    -0.02   0.983    -.4658999    .4558358
          8  |   .3064881   .1842345     1.66   0.099    -.0580514    .6710277
          9  |   .0437561   .1639462     0.27   0.790    -.2806395    .3681517
             |
   treatment |
          1  |  -.1960652   .1567702    -1.25   0.213    -.5062619    .1141314
          2  |  -.2630494   .1428177    -1.84   0.068    -.5456386    .0195398
          3  |  -.0788112   .1596633    -0.49   0.622    -.3947324      .23711
             |
       _cons |   .4882811   .1652263     2.96   0.004     .1613526    .8152097
------------------------------------------------------------------------------

. reg mmarried i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        977
                                                F(11, 128)        =       3.50
                                                Prob > F          =     0.0003
                                                R-squared         =     0.2381
                                                Root MSE          =     .39796

                                 (Std. err. adjusted for 129 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
    mmarried | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |  -.3901608   .2055235    -1.90   0.060    -.7968242    .0165027
          3  |  -.3678764   .2434225    -1.51   0.133    -.8495293    .1137765
          4  |  -.5684462   .1670818    -3.40   0.001    -.8990461   -.2378464
          5  |  -.3171282   .2498785    -1.27   0.207    -.8115554    .1772991
          6  |  -.6890468   .1424915    -4.84   0.000    -.9709906   -.4071029
          7  |  -.6592359   .1421829    -4.64   0.000    -.9405691   -.3779026
          8  |  -.5179825   .1658584    -3.12   0.002    -.8461616   -.1898033
          9  |  -.4643303    .158219    -2.93   0.004    -.7773936    -.151267
             |
   treatment |
          1  |  -.0517736   .1452247    -0.36   0.722    -.3391254    .2355783
          2  |  -.2073234   .1358599    -1.53   0.129    -.4761453    .0614986
          3  |  -.1448973   .1311901    -1.10   0.271    -.4044793    .1146847
             |
       _cons |   .8026004    .165004     4.86   0.000     .4761118    1.129089
------------------------------------------------------------------------------

. reg makan i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        977
                                                F(11, 128)        =      11.70
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2479
                                                Root MSE          =     .43174

                                 (Std. err. adjusted for 129 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
       makan | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |  -.3626883   .2291859    -1.58   0.116    -.8161718    .0907953
          3  |   -.021857   .1869329    -0.12   0.907    -.3917357    .3480216
          4  |   -.344146   .1839311    -1.87   0.064     -.708085    .0197931
          5  |  -.2097816   .2047431    -1.02   0.307    -.6149008    .1953376
          6  |  -.4536447   .1934469    -2.35   0.021    -.8364124   -.0708769
          7  |  -.7231283   .1326868    -5.45   0.000    -.9856719   -.4605848
          8  |   -.314656   .1736418    -1.81   0.072     -.658236    .0289241
          9  |   .0336072   .1466367     0.23   0.819    -.2565386     .323753
             |
   treatment |
          1  |   .1754808   .1370224     1.28   0.203    -.0956414    .4466031
          2  |  -.1755488   .1479562    -1.19   0.238    -.4683054    .1172078
          3  |   .1581254   .1306484     1.21   0.228    -.1003848    .4166356
             |
       _cons |   .6848536   .1644673     4.16   0.000      .359427     1.01028
------------------------------------------------------------------------------

. reg mage i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        977
                                                F(11, 128)        =       3.41
                                                Prob > F          =     0.0004
                                                R-squared         =     0.1461
                                                Root MSE          =     7.8701

                                 (Std. err. adjusted for 129 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
        mage | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |  -6.494261   2.256946    -2.88   0.005    -10.96001   -2.028507
          3  |   -4.64677   2.443401    -1.90   0.059    -9.481456    .1879156
          4  |  -7.339017   2.234972    -3.28   0.001    -11.76129   -2.916742
          5  |  -3.698003   4.511579    -0.82   0.414    -12.62493    5.228926
          6  |  -10.02035   2.250209    -4.45   0.000    -14.47277   -5.567924
          7  |   -10.5934   2.239113    -4.73   0.000    -15.02386   -6.162927
          8  |  -6.557753   2.340916    -2.80   0.006    -11.18966    -1.92585
          9  |  -6.812032    2.88865    -2.36   0.020    -12.52772   -1.096345
             |
   treatment |
          1  |  -.6640616   2.592074    -0.26   0.798    -5.792922    4.464799
          2  |   2.162755   2.378829     0.91   0.365    -2.544166    6.869675
          3  |  -1.535329   2.132301    -0.72   0.473    -5.754452    2.683793
             |
       _cons |   32.84336   2.451273    13.40   0.000      27.9931    37.69362
------------------------------------------------------------------------------

. reg mEducAny i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        977
                                                F(11, 128)        =       2.25
                                                Prob > F          =     0.0154
                                                R-squared         =     0.1342
                                                Root MSE          =       .435

                                 (Std. err. adjusted for 129 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
    mEducAny | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |   .1998187    .243322     0.82   0.413    -.2816355    .6812729
          3  |   .0656632   .2610164     0.25   0.802    -.4508024    .5821288
          4  |   .3139328   .2097234     1.50   0.137    -.1010408    .7289065
          5  |   .4426313   .1842543     2.40   0.018     .0780526    .8072099
          6  |   .5109679   .1506727     3.39   0.001     .2128362    .8090995
          7  |   .4191549    .180532     2.32   0.022     .0619416    .7763683
          8  |   .4647853   .1700158     2.73   0.007     .1283799    .8011907
          9  |   .1892204    .180296     1.05   0.296    -.1675261    .5459669
             |
   treatment |
          1  |  -.0185064   .1713331    -0.11   0.914    -.3575182    .3205054
          2  |    .057602   .1587708     0.36   0.717    -.2565532    .3717571
          3  |  -.0354148   .1514431    -0.23   0.815    -.3350709    .2642412
             |
       _cons |   .4382755   .1788452     2.45   0.016     .0843997    .7921513
------------------------------------------------------------------------------

. reg mselfemployed i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        977
                                                F(11, 128)        =      10.90
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1381
                                                Root MSE          =     .46189

                                 (Std. err. adjusted for 129 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
mselfemplo~d | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |  -.0203601   .2309058    -0.09   0.930    -.4772466    .4365264
          3  |   .2021917   .2114167     0.96   0.341    -.2161323    .6205157
          4  |  -.0507167   .2128399    -0.24   0.812    -.4718569    .3704234
          5  |   .5145067   .1488197     3.46   0.001     .2200416    .8089719
          6  |  -.0171765   .2398083    -0.07   0.943    -.4916782    .4573252
          7  |  -.2394084   .2078502    -1.15   0.252    -.6506755    .1718586
          8  |  -.2364527   .1827573    -1.29   0.198    -.5980692    .1251639
          9  |  -.1699767   .1769659    -0.96   0.339     -.520134    .1801806
             |
   treatment |
          1  |   .0552078   .1595075     0.35   0.730     -.260405    .3708205
          2  |   .0271066   .1565933     0.17   0.863    -.2827401    .3369533
          3  |  -.1256104   .1429666    -0.88   0.381    -.4084943    .1572734
             |
       _cons |    .513148   .1727785     2.97   0.004     .1712763    .8550198
------------------------------------------------------------------------------

. *reg mselfIncome i.treatment, cluster(ge02) //fine but too many already
. reg mbusTrained i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        977
                                                F(11, 128)        =      13.39
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2779
                                                Root MSE          =     .42639

                                 (Std. err. adjusted for 129 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
 mbusTrained | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |   .5664569   .1942355     2.92   0.004     .1821288    .9507851
          3  |  -.1749747   .1871379    -0.94   0.352    -.5452589    .1953096
          4  |   .1288781   .2177739     0.59   0.555    -.3020248    .5597809
          5  |   .7080602   .1250014     5.66   0.000     .4607236    .9553969
          6  |   .4145874   .2602454     1.59   0.114    -.1003525    .9295273
          7  |  -.1327032   .1581729    -0.84   0.403    -.4456754    .1802689
          8  |   .0574776   .1678567     0.34   0.733    -.2746556    .3896108
          9  |   .4439695   .1448089     3.07   0.003     .1574403    .7304987
             |
   treatment |
          1  |    .266982   .1627805     1.64   0.103     -.055107    .5890711
          2  |   .2588378   .1541262     1.68   0.096    -.0461271    .5638028
          3  |    .151091   .1357556     1.11   0.268    -.1175246    .4197066
             |
       _cons |   .1146106   .1423113     0.81   0.422    -.1669767    .3961978
------------------------------------------------------------------------------

. 
. **poverty?
. reg m4q3 i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        977
                                                F(11, 128)        =       2.54
                                                Prob > F          =     0.0061
                                                R-squared         =     0.1660
                                                Root MSE          =     1.4817

                                 (Std. err. adjusted for 129 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
        m4q3 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |   -.388067   .5328446    -0.73   0.468    -1.442391    .6662571
          3  |  -1.693698   1.109956    -1.53   0.129    -3.889936    .5025403
          4  |   .1274667   .3593825     0.35   0.723     -.583633    .8385664
          5  |  -.1634942   .5453086    -0.30   0.765     -1.24248    .9154919
          6  |   .4092867   .3117277     1.31   0.192    -.2075199    1.026093
          7  |   .4051301   .2909127     1.39   0.166    -.1704903    .9807505
          8  |   .1875205   .2948037     0.64   0.526     -.395799      .77084
          9  |  -1.169487   .5138483    -2.28   0.025    -2.186224   -.1527503
             |
   treatment |
          1  |  -.0473091   .4837569    -0.10   0.922    -1.004505    .9098865
          2  |   .1205434   .4581285     0.26   0.793    -.7859421    1.027029
          3  |   .2711548    .429273     0.63   0.529    -.5782352    1.120545
             |
       _cons |   4.486025   .5065116     8.86   0.000     3.483805    5.488245
------------------------------------------------------------------------------

. reg m4q4 i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        977
                                                F(11, 128)        =       2.94
                                                Prob > F          =     0.0017
                                                R-squared         =     0.0859
                                                Root MSE          =     2.1583

                                 (Std. err. adjusted for 129 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
        m4q4 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |   .9959581   .8413375     1.18   0.239    -.6687719    2.660688
          3  |    .188048   1.054984     0.18   0.859    -1.899417    2.275513
          4  |  -.8152426   1.092494    -0.75   0.457    -2.976929    1.346444
          5  |   1.422203   .7444721     1.91   0.058    -.0508617    2.895269
          6  |   1.408143   .7077766     1.99   0.049     .0076865      2.8086
          7  |   -.193708   1.124822    -0.17   0.864     -2.41936    2.031944
          8  |   .1388187   .8922133     0.16   0.877    -1.626578    1.904215
          9  |  -.3854377   .9030301    -0.43   0.670    -2.172237    1.401362
             |
   treatment |
          1  |   .1543173   .7537092     0.20   0.838    -1.337025     1.64566
          2  |  -.2052867   .6819002    -0.30   0.764    -1.554543    1.143969
          3  |  -.5291049   .7102862    -0.74   0.458    -1.934527    .8763177
             |
       _cons |     3.7804   .8294008     4.56   0.000     2.139288    5.421511
------------------------------------------------------------------------------

. reg m4q5 i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        977
                                                F(11, 128)        =       1.29
                                                Prob > F          =     0.2353
                                                R-squared         =     0.1302
                                                Root MSE          =     1.8424

                                 (Std. err. adjusted for 129 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
        m4q5 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |  -.3497778   1.239455    -0.28   0.778     -2.80225    2.102695
          3  |   .1993491   .8489815     0.23   0.815    -1.480506    1.879204
          4  |   .5468837   .5714847     0.96   0.340    -.5838965    1.677664
          5  |   1.249653   .6235298     2.00   0.047     .0158927    2.483413
          6  |   1.374638   .6551537     2.10   0.038     .0783042    2.670971
          7  |   .9596243   .6110468     1.57   0.119    -.2494362    2.168685
          8  |   .8225259   .6128705     1.34   0.182     -.390143    2.035195
          9  |  -.3448004   .7750061    -0.44   0.657    -1.878282    1.188682
             |
   treatment |
          1  |   .5228523   .5708123     0.92   0.361    -.6065974    1.652302
          2  |  -.4518298   .6514731    -0.69   0.489    -1.740881    .8372209
          3  |  -.4624274     .54211    -0.85   0.395    -1.535085    .6102298
             |
       _cons |   4.137175   .6647617     6.22   0.000     2.821831     5.45252
------------------------------------------------------------------------------

. reg m4q9 strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        977
                                                F(4, 128)         =       0.94
                                                Prob > F          =     0.4457
                                                R-squared         =     0.0318
                                                Root MSE          =     1.8354

                                 (Std. err. adjusted for 129 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
        m4q9 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |  -.0610393   .0535629    -1.14   0.257    -.1670227     .044944
             |
   treatment |
          1  |  -.0818452   .4742898    -0.17   0.863    -1.020309    .8566182
          2  |   .4321289   .4802209     0.90   0.370    -.5180702    1.382328
          3  |  -.3220178   .4495615    -0.72   0.475    -1.211552    .5675165
             |
       _cons |   9.183336   .3982752    23.06   0.000     8.395281    9.971392
------------------------------------------------------------------------------

. reg m4q10 i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        977
                                                F(11, 128)        =       3.06
                                                Prob > F          =     0.0011
                                                R-squared         =     0.1160
                                                Root MSE          =     3.2173

                                 (Std. err. adjusted for 129 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
       m4q10 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |   .7618845   1.855826     0.41   0.682    -2.910185    4.433954
          3  |   .5751429   2.181865     0.26   0.793     -3.74205    4.892335
          4  |  -2.511727   1.385782    -1.81   0.072    -5.253734    .2302796
          5  |   -3.26593   1.214615    -2.69   0.008    -5.669252   -.8626073
          6  |  -2.744166   1.317507    -2.08   0.039    -5.351079   -.1372541
          7  |   .1184074   1.798393     0.07   0.948    -3.440021    3.676836
          8  |  -.5405228   1.496563    -0.36   0.719    -3.501729    2.420683
          9  |  -1.620489   1.386884    -1.17   0.245    -4.364676    1.123697
             |
   treatment |
          1  |  -.0762077   .9581949    -0.08   0.937     -1.97216    1.819745
          2  |   .2684693   .9638128     0.28   0.781    -1.638599    2.175538
          3  |   .3180494   .9561059     0.33   0.740     -1.57377    2.209868
             |
       _cons |   3.297282   1.250776     2.64   0.009     .8224076    5.772157
------------------------------------------------------------------------------

. reg m_pov_likelihood i.strataFE i.treatment, cluster(ge02) //just report thi
> s index?

Linear regression                               Number of obs     =        990
                                                F(11, 129)        =       2.45
                                                Prob > F          =     0.0083
                                                R-squared         =     0.0969
                                                Root MSE          =      15.43

                                 (Std. err. adjusted for 130 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
m_pov_like~d | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |  -4.877243   4.459785    -1.09   0.276    -13.70104    3.946551
          3  |   1.520685   9.659447     0.16   0.875    -17.59077    20.63214
          4  |  -5.356861    4.79489    -1.12   0.266    -14.84367    4.129947
          5  |   -7.08167   4.234044    -1.67   0.097    -15.45883     1.29549
          6  |  -5.693621   4.727263    -1.20   0.231    -15.04663    3.659383
          7  |  -4.896295   4.447352    -1.10   0.273    -13.69549      3.9029
          8  |    3.52732   7.709401     0.46   0.648    -11.72592    18.78056
          9  |   5.727243   5.451508     1.05   0.295    -5.058698    16.51319
             |
   treatment |
          1  |   4.339864   5.942215     0.73   0.467    -7.416954    16.09668
          2  |   .5756646    4.28796     0.13   0.893     -7.90817    9.059499
          3  |   3.793812   4.039709     0.94   0.349    -4.198851    11.78647
             |
       _cons |   5.618678   6.386279     0.88   0.381    -7.016732    18.25409
------------------------------------------------------------------------------

. 
. 
. *reg dailyNobCustomers i.treatment, cluster(ge02) //fine but too many alread
> y
. *reg CustPer_w_Mkt strataFE i.treatment, cluster(ge02) //fine but too many a
> lready
. reg dailyTotMoney i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        977
                                                F(11, 128)        =       2.58
                                                Prob > F          =     0.0055
                                                R-squared         =     0.0595
                                                Root MSE          =     4391.2

                                 (Std. err. adjusted for 129 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
dailyTotMo~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |   -1643.25   1146.388    -1.43   0.154    -3911.574    625.0746
          3  |    45.9776   1697.572     0.03   0.978    -3312.959    3404.914
          4  |  -250.9483    1417.59    -0.18   0.860    -3055.892    2553.995
          5  |  -560.8527   1357.439    -0.41   0.680    -3246.777    2125.072
          6  |  -191.9635   1304.818    -0.15   0.883     -2773.77    2389.843
          7  |   525.5211   1251.208     0.42   0.675    -1950.207    3001.249
          8  |   2059.799   2789.528     0.74   0.462    -3459.758    7579.357
          9  |  -1037.224   1224.388    -0.85   0.399    -3459.885    1385.437
             |
   treatment |
          1  |   373.4414   851.0851     0.44   0.662    -1310.576    2057.459
          2  |   554.3935   975.8159     0.57   0.571    -1376.425    2485.212
          3  |   640.0283   1487.391     0.43   0.668     -2303.03    3583.086
             |
       _cons |     2034.9   1142.313     1.78   0.077     -225.362    4295.162
------------------------------------------------------------------------------

. reg dailyNobCustomers_nonM i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        716
                                                F(11, 101)        =       1.08
                                                Prob > F          =     0.3876
                                                R-squared         =     0.1121
                                                Root MSE          =     38.903

                                 (Std. err. adjusted for 102 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
dailyNobCu~M | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |      2.273   6.390447     0.36   0.723    -10.40393    14.94993
          3  |   48.56146   28.79305     1.69   0.095    -8.556207    105.6791
          4  |  -.4677663   9.592686    -0.05   0.961    -19.49708    18.56154
          5  |  -9.107414   8.333857    -1.09   0.277    -25.63954    7.424716
          6  |  -6.838812   10.86028    -0.63   0.530    -28.38269    14.70507
          7  |   27.90767   23.49909     1.19   0.238    -18.70819    74.52353
          8  |   6.697788   14.89489     0.45   0.654    -22.84967    36.24525
          9  |   8.893796   10.08086     0.88   0.380    -11.10392    28.89152
             |
   treatment |
          1  |  -2.246375   8.325652    -0.27   0.788    -18.76223    14.26948
          2  |  -7.728529   9.145543    -0.85   0.400    -25.87083    10.41377
          3  |   9.261916    13.0908     0.71   0.481    -16.70672    35.23055
             |
       _cons |   27.12713   8.289749     3.27   0.001      10.6825    43.57176
------------------------------------------------------------------------------

. reg dailyTotMoney_nonM i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        716
                                                F(11, 101)        =       2.82
                                                Prob > F          =     0.0030
                                                R-squared         =     0.1579
                                                Root MSE          =     156.11

                                 (Std. err. adjusted for 102 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
dailyTotMo~M | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |   131.6834   118.8628     1.11   0.271    -104.1085    367.4753
          3  |   216.7695   73.95628     2.93   0.004     70.06011    363.4789
          4  |   100.5064   54.80031     1.83   0.070    -8.202644    209.2155
          5  |  -14.09774   40.63868    -0.35   0.729    -94.71394    66.51846
          6  |  -40.38157   36.90746    -1.09   0.277     -113.596     32.8329
          7  |   36.32666   42.41362     0.86   0.394    -47.81054    120.4639
          8  |   3.031241   47.40342     0.06   0.949    -91.00439    97.06687
          9  |   105.1286   69.72905     1.51   0.135    -33.19504    243.4523
             |
   treatment |
          1  |  -26.12596   57.60007    -0.45   0.651     -140.389    88.13707
          2  |    9.99869   60.18606     0.17   0.868    -109.3943    129.3916
          3  |   26.65948    69.8862     0.38   0.704    -111.9759    165.2949
             |
       _cons |   101.5556    56.6621     1.79   0.076    -10.84675     213.958
------------------------------------------------------------------------------

. 
. **joint, exclude main Y
. reg trt0vsall mfemale mmarried makan mage mEducAny mselfemployed mselfIncome
>  mbusTrained, cluster(ge02)
note: mselfemployed omitted because of collinearity.

Linear regression                               Number of obs     =        416
                                                F(7, 54)          =       0.65
                                                Prob > F          =     0.7118
                                                R-squared         =     0.0947
                                                Root MSE          =      .3822

                                  (Std. err. adjusted for 55 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
   trt0vsall | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     mfemale |   .0167408    .126056     0.13   0.895    -.2359862    .2694677
    mmarried |  -.1005809   .0983059    -1.02   0.311    -.2976724    .0965105
       makan |   .1743498   .1422415     1.23   0.226    -.1108272    .4595268
        mage |  -.0020156    .003634    -0.55   0.581    -.0093013    .0052701
    mEducAny |   -.075323   .1304921    -0.58   0.566     -.336944    .1862979
mselfemplo~d |          0  (omitted)
 mselfIncome |   .0142603   .0422501     0.34   0.737    -.0704461    .0989668
 mbusTrained |   .0810283   .1197666     0.68   0.502    -.1590892    .3211458
       _cons |   .7694762   .2023663     3.80   0.000     .3637561    1.175196
------------------------------------------------------------------------------

. test mfemale mmarried makan mage mEducAny mselfemployed mselfIncome mbusTrai
> ned

 ( 1)  mfemale = 0
 ( 2)  mmarried = 0
 ( 3)  makan = 0
 ( 4)  mage = 0
 ( 5)  mEducAny = 0
 ( 6)  o.mselfemployed = 0
 ( 7)  mselfIncome = 0
 ( 8)  mbusTrained = 0
       Constraint 6 dropped

       F(  7,    54) =    0.65
            Prob > F =    0.7118

. 
. probit trt0vsall mfemale mmarried makan mage mEducAny mselfemployed mselfInc
> ome mbusTrained, cluster(ge02)

note: mselfemployed omitted because of collinearity.
Iteration 0:  Log pseudolikelihood =   -206.493  
Iteration 1:  Log pseudolikelihood = -187.03488  
Iteration 2:  Log pseudolikelihood =  -186.7202  
Iteration 3:  Log pseudolikelihood = -186.71962  
Iteration 4:  Log pseudolikelihood = -186.71962  

Probit regression                                       Number of obs =    416
                                                        Wald chi2(7)  =   6.05
                                                        Prob > chi2   = 0.5340
Log pseudolikelihood = -186.71962                       Pseudo R2     = 0.0958

                                  (Std. err. adjusted for 55 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
   trt0vsall | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     mfemale |   .1378952   .4795499     0.29   0.774    -.8020054    1.077796
    mmarried |  -.4121353   .4097322    -1.01   0.314    -1.215196    .3909251
       makan |   .5980701    .477502     1.25   0.210    -.3378167    1.533957
        mage |  -.0055614   .0196265    -0.28   0.777    -.0440287    .0329058
    mEducAny |  -.3441608   .4948295    -0.70   0.487    -1.314009    .6256872
mselfemplo~d |          0  (omitted)
 mselfIncome |   .0562209   .1603662     0.35   0.726    -.2580911    .3705329
 mbusTrained |    .314127   .4418379     0.71   0.477    -.5518594    1.180113
       _cons |   .8038032   .8830668     0.91   0.363    -.9269759    2.534582
------------------------------------------------------------------------------

. test mfemale mmarried makan mage mEducAny mselfemployed mselfIncome mbusTrai
> ned

 ( 1)  [trt0vsall]mfemale = 0
 ( 2)  [trt0vsall]mmarried = 0
 ( 3)  [trt0vsall]makan = 0
 ( 4)  [trt0vsall]mage = 0
 ( 5)  [trt0vsall]mEducAny = 0
 ( 6)  [trt0vsall]o.mselfemployed = 0
 ( 7)  [trt0vsall]mselfIncome = 0
 ( 8)  [trt0vsall]mbusTrained = 0
       Constraint 6 dropped

           chi2(  7) =    6.05
         Prob > chi2 =    0.5340

. 
. 
. 
. ** Table B.4 ---------------------------------------------------------------
> ----
. **Demand side: customers...?
. reg cfemale i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        989
                                                F(11, 129)        =       1.31
                                                Prob > F          =     0.2234
                                                R-squared         =     0.0195
                                                Root MSE          =     .48182

                                 (Std. err. adjusted for 130 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
     cfemale | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |   .0117712   .1060014     0.11   0.912    -.1979552    .2214976
          3  |  -.0025269   .0759968    -0.03   0.974    -.1528885    .1478346
          4  |   .1435777    .060648     2.37   0.019     .0235841    .2635712
          5  |   .1287237   .0884717     1.45   0.148    -.0463198    .3037672
          6  |  -.0025803   .1398498    -0.02   0.985    -.2792766     .274116
          7  |   .1182669   .0959442     1.23   0.220    -.0715611    .3080948
          8  |  -.0092875   .0606695    -0.15   0.879    -.1293236    .1107486
          9  |   .1221848   .0524525     2.33   0.021     .0184061    .2259634
             |
   treatment |
          1  |   .0009292   .0568927     0.02   0.987    -.1116344    .1134927
          2  |  -.0012118   .0646071    -0.02   0.985    -.1290384    .1266149
          3  |  -.0307095   .0581692    -0.53   0.598    -.1457987    .0843796
             |
       _cons |   .5703125    .058134     9.81   0.000      .455293     .685332
------------------------------------------------------------------------------

. reg cmarried i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        989
                                                F(11, 129)        =       1.78
                                                Prob > F          =     0.0639
                                                R-squared         =     0.0250
                                                Root MSE          =     .49522

                                 (Std. err. adjusted for 130 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
    cmarried | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |   -.048387   .0796356    -0.61   0.545     -.205948     .109174
          3  |   -.021937    .081482    -0.27   0.788    -.1831511    .1392771
          4  |  -.1178111   .0559505    -2.11   0.037    -.2285105   -.0071117
          5  |  -.2668874   .0937167    -2.85   0.005    -.4523082   -.0814666
          6  |   -.186987   .0834763    -2.24   0.027    -.3521469   -.0218271
          7  |  -.3144349   .1186376    -2.65   0.009    -.5491622   -.0797075
          8  |  -.0867737   .0604256    -1.44   0.153    -.2063272    .0327798
          9  |  -.1033251   .0458049    -2.26   0.026    -.1939513    -.012699
             |
   treatment |
          1  |   .0282674   .0479154     0.59   0.556    -.0665344    .1230691
          2  |  -.0000556   .0457219    -0.00   0.999    -.0905174    .0904062
          3  |   .0747151   .0520001     1.44   0.153    -.0281684    .1775985
             |
       _cons |    .606814   .0463049    13.10   0.000     .5151986    .6984294
------------------------------------------------------------------------------

. reg cakan i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        989
                                                F(11, 129)        =      14.25
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1760
                                                Root MSE          =     .44264

                                 (Std. err. adjusted for 130 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
       cakan | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |  -.1317512   .0786921    -1.67   0.097    -.2874455     .023943
          3  |   .0746798   .1156212     0.65   0.519    -.1540797    .3034393
          4  |  -.0761232   .0886938    -0.86   0.392     -.251606    .0993596
          5  |   .0879748   .0767659     1.15   0.254    -.0639084    .2398581
          6  |  -.6144113   .0854867    -7.19   0.000    -.7835489   -.4452737
          7  |  -.6325765   .1027789    -6.15   0.000     -.835927    -.429226
          8  |   -.363515   .1151055    -3.16   0.002     -.591254   -.1357759
          9  |  -.0023396   .0668574    -0.03   0.972    -.1346186    .1299394
             |
   treatment |
          1  |    .058568   .0834724     0.70   0.484    -.1065842    .2237202
          2  |   .0573803    .090679     0.63   0.528    -.1220304    .2367909
          3  |   .0718025   .0854109     0.84   0.402     -.097185    .2407901
             |
       _cons |   .6918363   .0995229     6.95   0.000     .4949278    .8887448
------------------------------------------------------------------------------

. reg cage i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        989
                                                F(11, 129)        =       2.64
                                                Prob > F          =     0.0044
                                                R-squared         =     0.0417
                                                Root MSE          =     14.981

                                 (Std. err. adjusted for 130 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
        cage | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |   2.078024   2.324734     0.89   0.373     -2.52152    6.677567
          3  |   1.412399   2.446937     0.58   0.565    -3.428925    6.253723
          4  |   7.711654   2.226237     3.46   0.001     3.306989    12.11632
          5  |   4.249954   2.197854     1.93   0.055    -.0985542    8.598463
          6  |  -1.167704   3.672183    -0.32   0.751    -8.433209    6.097801
          7  |   1.818566   3.177916     0.57   0.568    -4.469018     8.10615
          8  |   5.960689   2.032791     2.93   0.004     1.938762    9.982617
          9  |   6.501324   1.800757     3.61   0.000     2.938483    10.06416
             |
   treatment |
          1  |   1.541155   1.670839     0.92   0.358    -1.764642    4.846951
          2  |   .2531431   1.843863     0.14   0.891    -3.394985    3.901272
          3  |   .6449793   1.677413     0.38   0.701    -2.673823    3.963782
             |
       _cons |   35.27848   1.726275    20.44   0.000     31.86301    38.69396
------------------------------------------------------------------------------

. reg cEducAny i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        989
                                                F(11, 129)        =       1.86
                                                Prob > F          =     0.0513
                                                R-squared         =     0.0285
                                                Root MSE          =     .29763

                                 (Std. err. adjusted for 130 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
    cEducAny | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |   .0329736   .0304173     1.08   0.280    -.0272079     .093155
          3  |  -.0069067   .0404812    -0.17   0.865    -.0869997    .0731863
          4  |  -.0489331   .0341861    -1.43   0.155    -.1165711    .0187049
          5  |  -.1757765   .0588057    -2.99   0.003     -.292125    -.059428
          6  |  -.1348469   .0978451    -1.38   0.171    -.3284357     .058742
          7  |  -.0494859   .0968776    -0.51   0.610    -.2411607    .1421889
          8  |  -.0366295   .0311186    -1.18   0.241    -.0981985    .0249395
          9  |  -.0522503    .028501    -1.83   0.069    -.1086402    .0041395
             |
   treatment |
          1  |   .0287893   .0271006     1.06   0.290      -.02483    .0824085
          2  |  -.0278257    .039981    -0.70   0.488     -.106929    .0512776
          3  |    .019619   .0312353     0.63   0.531    -.0421809    .0814188
             |
       _cons |   .9378524   .0278359    33.69   0.000     .8827785    .9929264
------------------------------------------------------------------------------

. reg cselfemployed i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        989
                                                F(11, 129)        =       1.05
                                                Prob > F          =     0.4048
                                                R-squared         =     0.0207
                                                Root MSE          =     .45982

                                 (Std. err. adjusted for 130 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
cselfemplo~d | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |   .0792677   .0857837     0.92   0.357    -.0904575     .248993
          3  |  -.1102061    .136355    -0.81   0.420    -.3799877    .1595756
          4  |   .0403391   .0553295     0.73   0.467    -.0691316    .1498099
          5  |   .1153533   .1001482     1.15   0.252    -.0827924     .313499
          6  |  -.0382295   .1348582    -0.28   0.777    -.3050497    .2285907
          7  |   .0535673   .0876847     0.61   0.542     -.119919    .2270536
          8  |   .0537409   .0624079     0.86   0.391    -.0697346    .1772165
          9  |  -.0816613   .0616287    -1.33   0.187    -.2035952    .0402725
             |
   treatment |
          1  |   .0185271   .0497125     0.37   0.710    -.0798303    .1168845
          2  |    .057791    .062992     0.92   0.361    -.0668403    .1824222
          3  |   .0335745   .0546141     0.61   0.540    -.0744808    .1416298
             |
       _cons |   .6678847   .0559976    11.93   0.000     .5570921    .7786773
------------------------------------------------------------------------------

. *reg cselfIncome i.strataFE i.treatment, cluster(ge02) //fine but too manay 
> already
. reg cMMoneyregistered i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        989
                                                F(11, 129)        =       8.52
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0543
                                                Root MSE          =     .28831

                                 (Std. err. adjusted for 130 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
cMMoneyreg~d | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |   .0246953    .059448     0.42   0.679     -.092924    .1423146
          3  |   .1079418   .0200096     5.39   0.000     .0683522    .1475314
          4  |  -.1461035   .0509979    -2.86   0.005    -.2470042   -.0452028
          5  |   .1061044    .020044     5.29   0.000     .0664469     .145762
          6  |   .0652901   .0301647     2.16   0.032     .0056084    .1249718
          7  |   .0342152   .0473341     0.72   0.471    -.0594366    .1278669
          8  |   .0039444   .0299911     0.13   0.896    -.0553938    .0632826
          9  |   .0484116   .0282005     1.72   0.088    -.0073837    .1042069
             |
   treatment |
          1  |  -.0112947   .0315911    -0.36   0.721    -.0737984     .051209
          2  |  -.0050078   .0308656    -0.16   0.871    -.0660762    .0560606
          3  |    .002565   .0324972     0.08   0.937    -.0617314    .0668615
             |
       _cons |   .8968006   .0322207    27.83   0.000     .8330512    .9605501
------------------------------------------------------------------------------

. 
. **poverty?
. reg c2q3 i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        989
                                                F(11, 129)        =       6.53
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1053
                                                Root MSE          =     2.0563

                                 (Std. err. adjusted for 130 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
        c2q3 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |  -.9208358   .3369746    -2.73   0.007    -1.587548   -.2541234
          3  |   -.586076   .4531914    -1.29   0.198    -1.482726    .3105742
          4  |  -1.053829   .2829157    -3.72   0.000    -1.613584   -.4940731
          5  |  -.5078942   .3033909    -1.67   0.097    -1.108161    .0923721
          6  |   .5339194   .3079878     1.73   0.085     -.075442    1.143281
          7  |   .0866394   .4873548     0.18   0.859    -.8776041    1.050883
          8  |  -.2191594   .2448271    -0.90   0.372    -.7035557     .265237
          9  |  -1.516987   .2597769    -5.84   0.000    -2.030962   -1.003012
             |
   treatment |
          1  |   .0551135   .2364805     0.23   0.816     -.412769    .5229961
          2  |  -.2315738   .2515537    -0.92   0.359     -.729279    .2661314
          3  |   .2888071   .1934132     1.49   0.138    -.0938657    .6714799
             |
       _cons |   3.975724   .2254011    17.64   0.000     3.529763    4.421686
------------------------------------------------------------------------------

. reg c2q4 i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        989
                                                F(11, 129)        =       7.34
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1112
                                                Root MSE          =     2.2152

                                 (Std. err. adjusted for 130 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
        c2q4 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |   .9589656     .28176     3.40   0.001     .4014965    1.516435
          3  |   .3489166   .5585011     0.62   0.533    -.7560916    1.453925
          4  |  -1.483998   .5744514    -2.58   0.011    -2.620564   -.3474318
          5  |   .7375518   .3448828     2.14   0.034     .0551927    1.419911
          6  |   .5218863   .3200626     1.63   0.105    -.1113654    1.155138
          7  |   .1503074   .5066156     0.30   0.767    -.8520439    1.152659
          8  |  -.9645011   .4687593    -2.06   0.042    -1.891953   -.0370494
          9  |  -1.003525   .3863902    -2.60   0.010    -1.768008   -.2390429
             |
   treatment |
          1  |  -.1461507   .4426302    -0.33   0.742    -1.021906     .729604
          2  |   .2629507   .4471151     0.59   0.557    -.6216773    1.147579
          3  |   .2708218   .4521846     0.60   0.550    -.6238365     1.16548
             |
       _cons |   3.830146   .4180729     9.16   0.000     3.002979    4.657314
------------------------------------------------------------------------------

. reg c2q5 i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        989
                                                F(11, 129)        =       4.95
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1093
                                                Root MSE          =     1.9771

                                 (Std. err. adjusted for 130 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
        c2q5 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |    1.47213   .5545549     2.65   0.009     .3749298    2.569331
          3  |   .2018667    .421991     0.48   0.633    -.6330528    1.036786
          4  |   .7959893   .3415625     2.33   0.021     .1201996    1.471779
          5  |   1.710028   .3897831     4.39   0.000     .9388328    2.481224
          6  |   1.463156   .3848264     3.80   0.000     .7017679    2.224545
          7  |   1.413286    .472314     2.99   0.003     .4788015    2.347771
          8  |   .7408266   .4758786     1.56   0.122    -.2007109    1.682364
          9  |  -.2588168   .4733461    -0.55   0.585    -1.195344      .67771
             |
   treatment |
          1  |  -.3195771   .3048333    -1.05   0.296    -.9226973     .283543
          2  |  -.3083276   .3730674    -0.83   0.410    -1.046451    .4297955
          3  |  -.5751117   .2935252    -1.96   0.052    -1.155859    .0056351
             |
       _cons |   3.691435   .3215482    11.48   0.000     3.055243    4.327626
------------------------------------------------------------------------------

. reg c2q9 i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        988
                                                F(11, 128)        =       1.34
                                                Prob > F          =     0.2097
                                                R-squared         =     0.0244
                                                Root MSE          =      2.649

                                 (Std. err. adjusted for 129 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
        c2q9 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |    .242496   .3735249     0.65   0.517    -.4965868    .9815788
          3  |   .8736749   .5749063     1.52   0.131    -.2638755    2.011225
          4  |  -.2022268   .4152831    -0.49   0.627    -1.023935    .6194817
          5  |  -.9052317   .5323997    -1.70   0.092    -1.958675     .148212
          6  |    .034828   .3505194     0.10   0.921    -.6587346    .7283905
          7  |   .1185403   .5219659     0.23   0.821    -.9142583    1.151339
          8  |  -.5605152   .3171663    -1.77   0.080    -1.188083    .0670526
          9  |   .1832087    .266008     0.69   0.492    -.3431337    .7095511
             |
   treatment |
          1  |  -.4214574   .2891884    -1.46   0.147    -.9936661    .1507514
          2  |  -.0558506   .2894203    -0.19   0.847    -.6285182     .516817
          3  |   .0733586   .2996569     0.24   0.807    -.5195638    .6662809
             |
       _cons |   7.161389   .2732858    26.20   0.000     6.620646    7.702131
------------------------------------------------------------------------------

. reg c2q10 i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        988
                                                F(11, 128)        =       2.21
                                                Prob > F          =     0.0177
                                                R-squared         =     0.0334
                                                Root MSE          =     2.8247

                                 (Std. err. adjusted for 129 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
       c2q10 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |   -.889937   .6458146    -1.38   0.171    -2.167792    .3879176
          3  |   .3857182   .6709709     0.57   0.566    -.9419124    1.713349
          4  |  -1.177895   .4360832    -2.70   0.008     -2.04076   -.3150303
          5  |  -.1503496    .821665    -0.18   0.855    -1.776154    1.475455
          6  |  -.2164614   .7628041    -0.28   0.777      -1.7258    1.292877
          7  |  -.2355221    .622227    -0.38   0.706    -1.466704    .9956603
          8  |  -.2667339   .4925055    -0.54   0.589     -1.24124    .7077725
          9  |  -.9996813   .3720865    -2.69   0.008    -1.735918   -.2634446
             |
   treatment |
          1  |   .1454467   .3153568     0.46   0.645    -.4785406     .769434
          2  |   .3933422   .3406262     1.15   0.250    -.2806449    1.067329
          3  |   .4665619   .3881767     1.20   0.232     -.301512    1.234636
             |
       _cons |   1.711248   .4098508     4.18   0.000     .9002878    2.522207
------------------------------------------------------------------------------

. reg c_pov_likelihood i.strataFE i.treatment, cluster(ge02) //just report thi
> s index?

Linear regression                               Number of obs     =        990
                                                F(11, 129)        =       7.62
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0892
                                                Root MSE          =     14.144

                                 (Std. err. adjusted for 130 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
c_pov_like~d | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |  -7.298262   2.007074    -3.64   0.000    -11.26931   -3.327217
          3  |  -2.543383   3.363903    -0.76   0.451    -9.198948    4.112182
          4  |   .8641444   2.293441     0.38   0.707    -3.673485    5.401774
          5  |  -9.578768   2.193033    -4.37   0.000    -13.91774   -5.239797
          6  |  -7.662184   2.665312    -2.87   0.005    -12.93557   -2.388798
          7  |  -7.037264   2.345155    -3.00   0.003    -11.67721   -2.397318
          8  |  -.4839483   2.335159    -0.21   0.836    -5.104117     4.13622
          9  |   4.508947   2.311833     1.95   0.053    -.0650717    9.082965
             |
   treatment |
          1  |   1.973897   1.960574     1.01   0.316    -1.905146    5.852941
          2  |     .87357   2.010163     0.43   0.665    -3.103587    4.850727
          3  |  -.1276817    1.71866    -0.07   0.941    -3.528093     3.27273
             |
       _cons |   11.83243   2.209648     5.35   0.000     7.460587    16.20427
------------------------------------------------------------------------------

. **achieved strong balance on Trt vs Ctr...
. 
. 
. reg cfAttempts i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        988
                                                F(11, 128)        =       2.82
                                                Prob > F          =     0.0025
                                                R-squared         =     0.0507
                                                Root MSE          =     .48534

                                 (Std. err. adjusted for 129 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
  cfAttempts | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |   .0728865   .1100076     0.66   0.509    -.1447823    .2905554
          3  |   .0265544   .1306289     0.20   0.839    -.2319172     .285026
          4  |   -.180551   .0954083    -1.89   0.061    -.3693326    .0082305
          5  |   .2336869   .1018275     2.29   0.023     .0322038    .4351701
          6  |  -.3292423   .1268626    -2.60   0.011    -.5802615    -.078223
          7  |   .0546869    .118005     0.46   0.644    -.1788061    .2881799
          8  |  -.0157607   .0858823    -0.18   0.855    -.1856935    .1541722
          9  |  -.0902767   .0800524    -1.13   0.262     -.248674    .0681205
             |
   treatment |
          1  |  -.0200703   .0664719    -0.30   0.763    -.1515962    .1114556
          2  |   .0082096    .064572     0.13   0.899    -.1195571    .1359763
          3  |  -.0304106   .0652754    -0.47   0.642    -.1595691     .098748
             |
       _cons |   .6351909   .0729786     8.70   0.000     .4907904    .7795915
------------------------------------------------------------------------------

. reg _Xcfraud i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        988
                                                F(11, 128)        =       5.66
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0458
                                                Root MSE          =     .45277

                                 (Std. err. adjusted for 129 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
    _Xcfraud | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |  -.1440277   .0736994    -1.95   0.053    -.2898545     .001799
          3  |  -.0285746   .0673903    -0.42   0.672    -.1619179    .1047688
          4  |  -.2692157   .0567814    -4.74   0.000    -.3815673   -.1568641
          5  |  -.3046032   .0557877    -5.46   0.000    -.4149888   -.1942177
          6  |  -.3193938   .0630629    -5.06   0.000    -.4441746    -.194613
          7  |  -.1338916    .122375    -1.09   0.276    -.3760314    .1082481
          8  |   -.137382   .0593366    -2.32   0.022    -.2547896   -.0199743
          9  |  -.1334338   .0450035    -2.96   0.004     -.222481   -.0443866
             |
   treatment |
          1  |  -.0808832   .0497855    -1.62   0.107    -.1793923    .0176259
          2  |  -.0544446   .0496926    -1.10   0.275      -.15277    .0438807
          3  |   -.025663   .0520573    -0.49   0.623    -.1286672    .0773413
             |
       _cons |   .4916236    .044357    11.08   0.000     .4038557    .5793916
------------------------------------------------------------------------------

. 
. 
. reg distToBank i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        172
                                                F(11, 59)         =       3.96
                                                Prob > F          =     0.0003
                                                R-squared         =     0.1425
                                                Root MSE          =     1055.1

                                  (Std. err. adjusted for 60 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
  distToBank | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |   371.8624   178.3029     2.09   0.041     15.07913    728.6456
          3  |   2532.024   1585.867     1.60   0.116    -641.2888    5705.337
          4  |   456.4329   231.4194     1.97   0.053    -6.636251    919.5021
          5  |  -75.36905    124.686    -0.60   0.548    -324.8652    174.1271
          6  |   88.67539   140.3804     0.63   0.530    -192.2252    369.5759
          7  |   450.3745   164.1215     2.74   0.008     121.9682    778.7809
          8  |   23.28126    204.467     0.11   0.910    -385.8563    432.4188
          9  |   712.0318   235.8685     3.02   0.004       240.06    1184.004
             |
   treatment |
          1  |   45.59257    123.181     0.37   0.713     -200.892    292.0771
          2  |   195.2122   233.6986     0.84   0.407    -272.4176     662.842
          3  |   415.0574    189.657     2.19   0.033     35.55458    794.5602
             |
       _cons |  -87.15897   143.0441    -0.61   0.545    -373.3896    199.0717
------------------------------------------------------------------------------

. reg distToMMoney i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        925
                                                F(11, 126)        =       9.49
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2169
                                                Root MSE          =     76.014

                                 (Std. err. adjusted for 127 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
distToMMoney | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |   16.33279   12.38076     1.32   0.189    -8.168378    40.83395
          3  |   26.67662   24.45327     1.09   0.277    -21.71568    75.06892
          4  |   9.883354   8.636314     1.14   0.255    -7.207658    26.97437
          5  |    -11.636   6.553532    -1.78   0.078    -24.60525    1.333247
          6  |   75.89171   33.72063     2.25   0.026      9.15957    142.6239
          7  |   14.79968   6.923325     2.14   0.034     1.098626    28.50074
          8  |   52.90193   7.689909     6.88   0.000     37.68383    68.12004
          9  |   90.00801   14.05607     6.40   0.000     62.19147    117.8246
             |
   treatment |
          1  |   26.21104   16.55389     1.58   0.116    -6.548621     58.9707
          2  |  -7.900367   15.50396    -0.51   0.611    -38.58224    22.78151
          3  |  -.9600306   14.00899    -0.07   0.945    -28.68342    26.76335
             |
       _cons |   7.089162   10.86174     0.65   0.515    -14.40589    28.58422
------------------------------------------------------------------------------

. 
. 
. reg wklyTotUseVol i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        925
                                                F(11, 126)        =       6.29
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0108
                                                Root MSE          =     463.82

                                 (Std. err. adjusted for 127 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
wklyTotUse~l | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |   35.74149   78.81667     0.45   0.651    -120.2344    191.7174
          3  |  -30.09192   44.33676    -0.68   0.499    -117.8331    57.64923
          4  |   1.569133   69.47887     0.02   0.982    -135.9275    139.0658
          5  |   -170.835    37.4272    -4.56   0.000    -244.9023   -96.76769
          6  |  -71.10011   40.96221    -1.74   0.085    -152.1631      9.9629
          7  |  -16.01555   53.55542    -0.30   0.765    -122.0001    89.96904
          8  |  -48.07431   69.94285    -0.69   0.493    -186.4892    90.34053
          9  |  -22.99536   38.62182    -0.60   0.553    -99.42682    53.43609
             |
   treatment |
          1  |  -29.29557   39.50928    -0.74   0.460    -107.4833    48.89213
          2  |  -5.371622   39.15604    -0.14   0.891    -82.86028    72.11704
          3  |    43.7351   54.56504     0.80   0.424    -64.24752    151.7177
             |
       _cons |   182.4607   50.64165     3.60   0.000     82.24233     282.679
------------------------------------------------------------------------------

. reg wklyNobUsage_nonM i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        988
                                                F(11, 128)        =       5.07
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0056
                                                Root MSE          =     17.519

                                 (Std. err. adjusted for 129 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
wklyNobUsa~M | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |  -.2286304   1.869973    -0.12   0.903    -3.928691     3.47143
          3  |  -2.140279   1.638192    -1.31   0.194    -5.381722    1.101163
          4  |  -1.265712   1.703228    -0.74   0.459    -4.635841    2.104416
          5  |  -4.050566   1.631482    -2.48   0.014    -7.278731   -.8224006
          6  |  -3.539325   1.554942    -2.28   0.024    -6.616042   -.4626069
          7  |  -1.404212   1.670982    -0.84   0.402    -4.710535    1.902112
          8  |   -3.04001   1.580803    -1.92   0.057    -6.167899    .0878798
          9  |   -1.20575   2.110637    -0.57   0.569    -5.382006    2.970507
             |
   treatment |
          1  |  -.2797207   .7288527    -0.38   0.702     -1.72188    1.162439
          2  |   1.079325   1.772852     0.61   0.544    -2.428566    4.587215
          3  |   .6110474   1.160223     0.53   0.599    -1.684653    2.906747
             |
       _cons |   3.666494   1.431697     2.56   0.012      .833637    6.499351
------------------------------------------------------------------------------

. reg wklyTotUseVol_nonM i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        988
                                                F(11, 128)        =       3.42
                                                Prob > F          =     0.0003
                                                R-squared         =     0.0072
                                                Root MSE          =     278.74

                                 (Std. err. adjusted for 129 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
wklyTotUse~M | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |  -23.14441   45.53342    -0.51   0.612    -113.2401    66.95125
          3  |  -49.45462   42.24363    -1.17   0.244    -133.0409    34.13163
          4  |  -27.83981   52.84268    -0.53   0.599    -132.3981    76.71847
          5  |  -70.86412   38.74843    -1.83   0.070    -147.5345    5.806258
          6  |  -47.59987    36.8426    -1.29   0.199    -120.4992     25.2995
          7  |  -54.83668   37.68654    -1.46   0.148     -129.406    19.73258
          8  |  -47.96238   40.42819    -1.19   0.238    -127.9565     32.0317
          9  |  -10.28981    40.5384    -0.25   0.800    -90.50196    69.92235
             |
   treatment |
          1  |    31.2253     28.401     1.10   0.274    -24.97092    87.42152
          2  |   18.69932   17.46658     1.07   0.286    -15.86129    53.25994
          3  |   18.75567   21.80016     0.86   0.391    -24.37967    61.89102
             |
       _cons |   52.12873   35.87812     1.45   0.149    -18.86227    123.1197
------------------------------------------------------------------------------

. 
. reg likelyborrowMMoney i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        988
                                                F(11, 128)        =       5.98
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0698
                                                Root MSE          =     .88111

                                 (Std. err. adjusted for 129 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
likelyborr~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |   .6330072   .1542698     4.10   0.000      .327758    .9382564
          3  |   .6482757   .3106107     2.09   0.039     .0336793    1.262872
          4  |    .167657    .178566     0.94   0.350    -.1856663    .5209804
          5  |   .6247069   .1028276     6.08   0.000     .4212449     .828169
          6  |   .3958376   .2314355     1.71   0.090     -.062097    .8537722
          7  |   .6975107   .2838869     2.46   0.015     .1357919    1.259229
          8  |   .1648938   .0962942     1.71   0.089    -.0256406    .3554282
          9  |   .5103129   .1359188     3.75   0.000     .2413743    .7792514
             |
   treatment |
          1  |  -.0201218   .1268604    -0.16   0.874    -.2711369    .2308932
          2  |   .0543415   .1575378     0.34   0.731    -.2573741     .366057
          3  |   .0947641   .1671071     0.57   0.572    -.2358859    .4254141
             |
       _cons |   1.083466    .107141    10.11   0.000      .871469    1.295463
------------------------------------------------------------------------------

. reg likelysaveMMoney i.strataFE i.treatment, cluster(ge02)

Linear regression                               Number of obs     =        988
                                                F(11, 128)        =      17.55
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2100
                                                Root MSE          =     1.1295

                                 (Std. err. adjusted for 129 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
likelysave~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    strataFE |
          2  |   1.481537   .2464827     6.01   0.000      .993829    1.969245
          3  |   1.130492   .2837221     3.98   0.000     .5690991    1.691884
          4  |   .3741452   .1742861     2.15   0.034     .0292905        .719
          5  |   1.518666   .1468851    10.34   0.000     1.228029    1.809303
          6  |   .7088607   .3097672     2.29   0.024     .0959334    1.321788
          7  |   .6005665   .2869791     2.09   0.038     .0327293    1.168404
          8  |   .3531525   .1527122     2.31   0.022     .0509854    .6553197
          9  |   1.388607   .1772755     7.83   0.000     1.037838    1.739377
             |
   treatment |
          1  |  -.1223041   .1597235    -0.77   0.445    -.4383444    .1937361
          2  |  -.0553366   .1577631    -0.35   0.726    -.3674979    .2568247
          3  |  -.0178239   .1752782    -0.10   0.919    -.3646418     .328994
             |
       _cons |   1.405123   .1825993     7.70   0.000     1.043819    1.766427
------------------------------------------------------------------------------

. 
. 
. **joint, exclude main Y?
. reg trt0vsall cfemale cmarried cakan cage cEducAny cselfemployed cselfIncome
>  cMMoneyregistered, cluster(ge02)

Linear regression                               Number of obs     =        989
                                                F(8, 129)         =       0.51
                                                Prob > F          =     0.8506
                                                R-squared         =     0.0079
                                                Root MSE          =     .38939

                                 (Std. err. adjusted for 130 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
   trt0vsall | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     cfemale |  -.0117052    .038162    -0.31   0.760    -.0872095    .0637992
    cmarried |   .0172191   .0320848     0.54   0.592    -.0462614    .0806996
       cakan |   .0554982   .0536433     1.03   0.303    -.0506363    .1616327
        cage |   .0004831   .0011622     0.42   0.678    -.0018164    .0027825
    cEducAny |   .0120234   .0462434     0.26   0.795    -.0794702    .1035171
cselfemplo~d |   .0104762    .037323     0.28   0.779    -.0633683    .0843206
 cselfIncome |  -.0160774   .0313857    -0.51   0.609    -.0781748    .0460199
cMMoneyreg~d |    .015228   .0574335     0.27   0.791    -.0984056    .1288617
       _cons |   .7470232   .1028164     7.27   0.000     .5435984    .9504479
------------------------------------------------------------------------------

. test cfemale cmarried cakan cage cEducAny cselfemployed cselfIncome cMMoneyr
> egistered

 ( 1)  cfemale = 0
 ( 2)  cmarried = 0
 ( 3)  cakan = 0
 ( 4)  cage = 0
 ( 5)  cEducAny = 0
 ( 6)  cselfemployed = 0
 ( 7)  cselfIncome = 0
 ( 8)  cMMoneyregistered = 0

       F(  8,   129) =    0.51
            Prob > F =    0.8506

. probit trt0vsall cfemale cakan cmarried cage cEducAny cselfemployed cselfInc
> ome cMMoneyregistered, cluster(ge02)

Iteration 0:  Log pseudolikelihood = -475.15448  
Iteration 1:  Log pseudolikelihood = -471.35922  
Iteration 2:  Log pseudolikelihood =   -471.356  
Iteration 3:  Log pseudolikelihood =   -471.356  

Probit regression                                       Number of obs =    989
                                                        Wald chi2(8)  =   4.11
                                                        Prob > chi2   = 0.8468
Log pseudolikelihood = -471.356                         Pseudo R2     = 0.0080

                                 (Std. err. adjusted for 130 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
   trt0vsall | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     cfemale |  -.0356721   .1384725    -0.26   0.797    -.3070732     .235729
       cakan |   .2032502   .1886439     1.08   0.281     -.166485    .5729854
    cmarried |   .0682004    .121122     0.56   0.573    -.1691943    .3055951
        cage |     .00167   .0044996     0.37   0.711    -.0071491    .0104891
    cEducAny |   .0402874   .1660675     0.24   0.808    -.2851989    .3657738
cselfemplo~d |   .0409868   .1353492     0.30   0.762    -.2242926    .3062663
 cselfIncome |  -.0566756   .1065079    -0.53   0.595    -.2654272    .1520759
cMMoneyreg~d |   .0529316   .2043192     0.26   0.796    -.3475267    .4533899
       _cons |   .6524309   .3561292     1.83   0.067    -.0455695    1.350431
------------------------------------------------------------------------------

. test cfemale cmarried cakan cage cEducAny cselfemployed cselfIncome cMMoneyr
> egistered

 ( 1)  [trt0vsall]cfemale = 0
 ( 2)  [trt0vsall]cmarried = 0
 ( 3)  [trt0vsall]cakan = 0
 ( 4)  [trt0vsall]cage = 0
 ( 5)  [trt0vsall]cEducAny = 0
 ( 6)  [trt0vsall]cselfemployed = 0
 ( 7)  [trt0vsall]cselfIncome = 0
 ( 8)  [trt0vsall]cMMoneyregistered = 0

           chi2(  8) =    4.11
         Prob > chi2 =    0.8468

. 
. 
. 
. 
. 
. 
. 
end of do-file

. 
. do "$do_loc/Beliefs_Mar.19.2023.do" // 1-ish minute

. /*
> JPE2023-Annan
> y = beliefs*
> 
> Input:
>         - FFPhone in 2020/Customer_+_Mktcensus_+_Interventions.dta
>         - FINAL AUDIT DATA/_Francis/ofdrate_mktAudit_endline.dta
> Output: 
>         - FFPhone in 2020/_impact-evaluation/te_belief_all_graph.eps
>         - FFPhone in 2020/_impact-evaluation/te_belief_pt_graph.eps
>         - FFPhone in 2020/_impact-evaluation/te_belief_m&r_graph.eps
>         - FFPhone in 2020/_impact-evaluation/te_belief_both_graph.eps
> */
. 
. **Consumers subjective beliefs: shifts + updates*
. use "$dta_loc_repl/02_final/Customer_+_Mktcensus_+_Interventions.dta", clear

. 
. drop _merge

. *drop if missing(_customer2020_id)
. **bring in audit-objective endline data: "use sep 06 fd data"
. merge m:1 ge01 ge02 using "$dta_loc_repl/01_intermediate/ofdrate_mktAudit_en
> dline.dta"

    Result                      Number of obs
    -----------------------------------------
    Not matched                            20
        from master                        20  (_merge==1)
        from using                          0  (_merge==2)

    Matched                               970  (_merge==3)
    -----------------------------------------

. *drop if missing(_customer2020_id)
. gen dropout_belief = missing(customer2020_id) // PII but used as marker

. tab dropout_belief

dropout_bel |
        ief |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        810       81.82       81.82
          1 |        180       18.18      100.00
------------+-----------------------------------
      Total |        990      100.00

. 
. // encode ge01, gen(districtID)
. gen districtID = ge01

. 
. **views now about misconduct in dxn of info assignments?
. **e.g., perceive misconduct is low? "correctly" perceive others in locality 
> perceive misconduct low?
. tab c8a

 C8a  In my |
      view, |
    general |
 misconduct |
         or |
overchargin |
          g |
 customers? |
transaction |
     s at M |      Freq.     Percent        Cum.
------------+-----------------------------------
      Agree |        489       60.37       60.37
   Disagree |        321       39.63      100.00
------------+-----------------------------------
      Total |        810      100.00

. tab c8b

C8b  What?s |
       your |
estimate of |
   the % of |
others (all |
vendors and |
  customers |
   in this  |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         48        5.93        5.93
          1 |          1        0.12        6.05
          5 |         36        4.44       10.49
         10 |         54        6.67       17.16
         15 |          1        0.12       17.28
         20 |         80        9.88       27.16
         30 |         57        7.04       34.20
         40 |         24        2.96       37.16
         50 |        109       13.46       50.62
         60 |         91       11.23       61.85
         70 |        132       16.30       78.15
         75 |          2        0.25       78.40
         80 |         90       11.11       89.51
         85 |          2        0.25       89.75
         90 |         81       10.00       99.75
        100 |          2        0.25      100.00
------------+-----------------------------------
      Total |        810      100.00

. tab c4

    C4  Any |
experiences |
         of |
overcharged |
    M-Money |
    fees at |
    M-Money |
    centers |
 within the |
         pa |      Freq.     Percent        Cum.
------------+-----------------------------------
        Yes |        650       80.25       80.25
         No |        160       19.75      100.00
------------+-----------------------------------
      Total |        810      100.00

. tab c8q3 //baseline belief (ok)

     Do you |
      think |
 vendors in |
  the local |
     market |
 overcharge |
    M-Money |
  and other |
  financial |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        431       43.62       43.62
          2 |        557       56.38      100.00
------------+-----------------------------------
      Total |        988      100.00

. 
. tab date_of_interview trt

   Date of |                     trt
 Interview |         0          1          2          3 |     Total
-----------+--------------------------------------------+----------
   1052020 |         3         28          1         30 |        62 
   2052020 |         8          9         10          2 |        29 
   3052020 |        21         14          0         23 |        58 
   4052020 |         7         13          4         27 |        51 
   5052020 |         2          1         21          6 |        30 
   6052020 |         1         10         28         10 |        49 
   7052020 |         8         12          6          4 |        30 
   8052020 |        15         17          5         14 |        51 
   9052020 |         5          9          5          5 |        24 
  1.01e+07 |         5          5          0          0 |        10 
  1.11e+07 |         6          8         15         18 |        47 
  1.21e+07 |         0          1          0          0 |         1 
  1.41e+07 |         0          0          2          0 |         2 
  1.51e+07 |         0          0          1          1 |         2 
  1.61e+07 |         3          1          0          0 |         4 
  2.20e+07 |        13         14          0          5 |        32 
  2.30e+07 |         2          3          1          3 |         9 
  2.40e+07 |        14          4          1         12 |        31 
  2.50e+07 |         7         19         12          7 |        45 
  2.60e+07 |         0         14         15          7 |        36 
  2.70e+07 |         3         13         25          7 |        48 
  2.80e+07 |         7          5         40         15 |        67 
  2.90e+07 |        10         16          9         20 |        55 
  3.00e+07 |         3         14          6         14 |        37 
-----------+--------------------------------------------+----------
     Total |       143        230        207        230 |       810 

. 
. gen dhonestVendors1=(c8a==1) if dropout_belief==0 //i agree to misconduct: n
> ot incentivized
(180 missing values generated)

. gen dhonestVendors2=c8b if dropout_belief==0 //incentivized (% agree for dis
> honest vendors)
(180 missing values generated)

. gen dhonestVendors3=(c4==1) if dropout_belief==0 //i think experiencing it (
> yes)
(180 missing values generated)

. gen dhonestVendors4=(c8a==1 | c4==1) if dropout_belief==0
(180 missing values generated)

. pwcorr dhonestVendors*, sig

             | dhones~1 dhones~2 dhones~3 dhones~4
-------------+------------------------------------
dhonestVen~1 |   1.0000 
             |
             |
dhonestVen~2 |   0.8063   1.0000 
             |   0.0000
             |
dhonestVen~3 |   0.4348   0.3917   1.0000 
             |   0.0000   0.0000
             |
dhonestVen~4 |   0.5446   0.4675   0.8893   1.0000 
             |   0.0000   0.0000   0.0000
             |

. sum dhonestVendors* if trtment==0 & dropout_belief==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
dhonestVen~1 |        143    .6853147    .4660227          0          1
dhonestVen~2 |        143    56.25874    28.31207          0        100
dhonestVen~3 |        143    .8461538    .3620694          0          1
dhonestVen~4 |        143    .8811189    .3247862          0          1

. 
. 
. gen honestVendors1=(c8a==2) if dropout_belief==0 //i disagree misconduct: no
> t incentivized
(180 missing values generated)

. gen honestVendors2=100-c8b if dropout_belief==0 //incentivized (% WONT agree
>  dishonest vendors)
(180 missing values generated)

. gen honestVendors3=(c4==2) if dropout_belief==0 //i think experiencing it (n
> o)
(180 missing values generated)

. gen honestVendors4=(c8a==2 | c4==2) if dropout_belief==0
(180 missing values generated)

. pwcorr honestVendors*, sig

             | honest~1 honest~2 honest~3 honest~4
-------------+------------------------------------
honestVend~1 |   1.0000 
             |
             |
honestVend~2 |   0.8063   1.0000 
             |   0.0000
             |
honestVend~3 |   0.4348   0.3917   1.0000 
             |   0.0000   0.0000
             |
honestVend~4 |   0.9312   0.7627   0.5702   1.0000 
             |   0.0000   0.0000   0.0000
             |

. sum honestVendors* if trtment==0 & dropout_belief==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
honestVend~1 |        143    .3146853    .4660227          0          1
honestVend~2 |        143    43.74126    28.31207          0        100
honestVend~3 |        143    .1538462    .3620694          0          1
honestVend~4 |        143    .3496503    .4785356          0          1

. 
. *********
. *n=792 vs n=810 (ok)
. tab _merge

   Matching result from |
                  merge |      Freq.     Percent        Cum.
------------------------+-----------------------------------
        Master only (1) |         20        2.02        2.02
            Matched (3) |        970       97.98      100.00
------------------------+-----------------------------------
                  Total |        990      100.00

. egen uniqueLocalityID = group(ge01 ge02)

. 
. **********************************************
. *q.1: beliefs shifted in right direction, yes?
. sum honestVendors1 if trtment==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
honestVend~1 |        143    .3146853    .4660227          0          1

. tab trt if !missing(trt), gen(trt) //gen trts again and verifY

        trt |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        185       18.69       18.69
          1 |        272       27.47       46.16
          2 |        257       25.96       72.12
          3 |        276       27.88      100.00
------------+-----------------------------------
      Total |        990      100.00

. gen trt01 = (trt !=0) if !missing(trt)

. 
. 
. ** Table 3 -----------------------------------------------------------------
> ----
. *reg honestVendors1 i.districtID i.c8q3 cfemale cage cmarried cakan cselfemp
> loyed cEducAny cselfIncome trt01 if dropout_belief==0, level(95) r
. reg honestVendors1 i.districtID i.c8q3 cfemale cage cmarried cakan cselfempl
> oyed cEducAny cselfIncome trtment if dropout_belief==0, level(95) r

Linear regression                               Number of obs     =        810
                                                F(17, 792)        =      15.57
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2095
                                                Root MSE          =     .43981

------------------------------------------------------------------------------
             |               Robust
honestVend~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |   .1257279    .080793     1.56   0.120    -.0328659    .2843217
          3  |  -.0217072   .0930508    -0.23   0.816    -.2043625    .1609482
          4  |   .0114701   .0615724     0.19   0.852    -.1093944    .1323345
          5  |   .4024456   .1006987     4.00   0.000     .2047777    .6001135
          6  |   .1858393    .086451     2.15   0.032     .0161391    .3555395
          7  |  -.1772037   .0585414    -3.03   0.003    -.2921182   -.0622891
          8  |  -.0330669   .0502697    -0.66   0.511    -.1317445    .0656107
          9  |   .4439143   .0498802     8.90   0.000     .3460012    .5418274
             |
      2.c8q3 |   .0229416   .0359348     0.64   0.523    -.0475972    .0934804
     cfemale |   .0079746   .0328584     0.24   0.808    -.0565253    .0724744
        cage |   .0003832   .0011502     0.33   0.739    -.0018746     .002641
    cmarried |   .0099201   .0345535     0.29   0.774    -.0579072    .0777473
       cakan |  -.0141413   .0348768    -0.41   0.685    -.0826033    .0543206
cselfemplo~d |   .0079067   .0364924     0.22   0.829    -.0637266    .0795401
    cEducAny |  -.0337944   .0555518    -0.61   0.543    -.1428406    .0752519
 cselfIncome |   -.018469   .0234447    -0.79   0.431      -.06449    .0275521
     trtment |    .066966    .040017     1.67   0.095     -.011586    .1455181
       _cons |   .1918275   .0942345     2.04   0.042     .0068486    .3768064
------------------------------------------------------------------------------

. *reg honestVendors1 i.districtID i.c8q3 cfemale cage cmarried cakan cselfemp
> loyed cEducAny cselfIncome trt2 trt3 trt4 if dropout_belief==0, level(95) r 
> cluster(uniqueLocalityID)
. reg honestVendors1 i.districtID i.c8q3 cfemale cage cmarried cakan cselfempl
> oyed cEducAny cselfIncome i.trt if dropout_belief==0, level(95) r cluster(un
> iqueLocalityID)

Linear regression                               Number of obs     =        810
                                                F(19, 124)        =      10.28
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2284
                                                Root MSE          =     .43506

                     (Std. err. adjusted for 125 clusters in uniqueLocalityID)
------------------------------------------------------------------------------
             |               Robust
honestVend~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |   .1208371   .0772385     1.56   0.120    -.0320396    .2737138
          3  |  -.0226131   .1208142    -0.19   0.852    -.2617383    .2165122
          4  |  -.0033355   .0714978    -0.05   0.963    -.1448496    .1381786
          5  |   .3962387   .1089727     3.64   0.000     .1805512    .6119263
          6  |   .2065302   .0999386     2.07   0.041     .0087236    .4043367
          7  |  -.1771228    .073612    -2.41   0.018    -.3228216    -.031424
          8  |  -.0342844   .0750682    -0.46   0.649    -.1828655    .1142966
          9  |    .454448   .0763689     5.95   0.000     .3032925    .6056034
             |
      2.c8q3 |   .0261275   .0407273     0.64   0.522    -.0544833    .1067383
     cfemale |   .0063692   .0308785     0.21   0.837     -.054748    .0674864
        cage |   .0004249    .001156     0.37   0.714    -.0018632     .002713
    cmarried |    .004266    .038074     0.11   0.911     -.071093    .0796251
       cakan |  -.0137059   .0357539    -0.38   0.702     -.084473    .0570611
cselfemplo~d |   .0119246   .0325696     0.37   0.715    -.0525398    .0763889
    cEducAny |  -.0472899   .0541773    -0.87   0.384     -.154522    .0599422
 cselfIncome |   -.019526    .020722    -0.94   0.348    -.0605407    .0214888
             |
         trt |
          1  |   .1067992    .057862     1.85   0.067     -.007726    .2213243
          2  |  -.0450068   .0575898    -0.78   0.436    -.1589932    .0689796
          3  |   .1262929   .0543058     2.33   0.022     .0188064    .2337794
             |
       _cons |   .2010583   .1027613     1.96   0.053    -.0023351    .4044517
------------------------------------------------------------------------------

. test 1.trt=3.trt

 ( 1)  1.trt - 3.trt = 0

       F(  1,   124) =    0.10
            Prob > F =    0.7473

. test 2.trt=3.trt

 ( 1)  2.trt - 3.trt = 0

       F(  1,   124) =    7.97
            Prob > F =    0.0056

. test 1.trt=2.trt

 ( 1)  1.trt - 2.trt = 0

       F(  1,   124) =    5.31
            Prob > F =    0.0229

. test 1.trt+2.trt=3.trt

 ( 1)  1.trt + 2.trt - 3.trt = 0

       F(  1,   124) =    0.62
            Prob > F =    0.4322

. 
. ** Figure 1 ----------------------------------------------------------------
> ----
. *q.1: beliefs shifted in right direction, yes - graphically?
. bys ge01 ge02: egen mx = mean(honestVendors1) if dropout_belief==0
(180 missing values generated)

. bys trt: sum mx

------------------------------------------------------------------------------
-> trt = 0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
          mx |        143    .3146853    .2771915          0          1

------------------------------------------------------------------------------
-> trt = 1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
          mx |        230    .4304348     .284116          0          1

------------------------------------------------------------------------------
-> trt = 2

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
          mx |        207    .3429952     .279895          0          1

------------------------------------------------------------------------------
-> trt = 3

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
          mx |        230    .4608696    .3601974          0          1


. cdfplot mx if (trt==0 | trt==1 | trt==2 | trt==3), by(trtment) opt1(lc() lp(
> solid dash)) xtitle("Share that perceive vendors are honest") ytitle("Cumula
> tive Probability") legend(pos(7) row(1) stack label(1 "Control") label(2 "An
> y treatment"))
(0 observations deleted)

. gr export "$output_loc/main_results/te_belief_all_graph.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/main_results/te_belief_all_graph.eps saved as EPS format

. ksmirnov mx, by(trtment) exact //p-val=0.000

Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value      Exact
--------------------------------------------------
0                    0.2875       0.000
1                   -0.0236       0.877
Combined K-S         0.2875       0.000      0.000

Note: Ties exist in combined dataset;
      there are 39 unique values out of 810 observations.

. 
. *(1) voxdev
. bys trtment: sum mx

------------------------------------------------------------------------------
-> trtment = 0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
          mx |        143    .3146853    .2771915          0          1

------------------------------------------------------------------------------
-> trtment = 1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
          mx |        667    .4137931    .3146702          0          1


. quietly eststo Control: mean mx if trtment==0

. quietly eststo Treatment: mean mx if trtment==1

. coefplot Control Treatment, vertical xlabel("") xtitle(Share that perceive v
> endors are honest) ytitle(Mean) recast(bar) barwidth(0.25) fcolor(*.5) ciopt
> s(recast(rcap)) citop citype(logit) level(95)  graphregion(color(white)) yla
> b(,nogrid)

. gr export $output_loc/main_results/gr_conduct_perceptions.eps, replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/main_results/gr_conduct_perceptions.eps saved as EPS format

. gr save $output_loc/main_results/gr_conduct_perceptions, replace
file /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_cop
> y/output/main_results/gr_conduct_perceptions.gph saved

. 
. 
. cdfplot mx if (trt==0 | trt==1), by(trtment) opt1(lc() lp(solid dash)) xtitl
> e("Share that perceive vendors are honest") ytitle("Cumulative Probability")
>  legend(pos(7) row(1) stack label(1 "Control") label(2 "Transparency alone (
> PT)"))
(0 observations deleted)

. gr export "$output_loc/main_results/te_belief_pt_graph.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/main_results/te_belief_pt_graph.eps saved as EPS format

. ksmirnov mx if (trt==0 | trt==1), by(trtment) exact //p-val=0.000

Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value      Exact
--------------------------------------------------
0                    0.3365       0.000
1                   -0.0089       0.986
Combined K-S         0.3365       0.000      0.000

Note: Ties exist in combined dataset;
      there are 25 unique values out of 373 observations.

. 
. cdfplot mx if (trt==0 | trt==2), by(trtment) opt1(lc() lp(solid dash)) xtitl
> e("Share that perceive vendors are honest") ytitle("Cumulative Probability")
>  legend(pos(7) row(1) stack label(1 "Control") label(2 "Monitoring alone (MR
> )"))
(0 observations deleted)

. gr export "$output_loc/main_results/te_belief_m&r_graph.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/main_results/te_belief_m&r_graph.eps saved as EPS format

. ksmirnov mx if (trt==0 | trt==2), by(trtment) exact //p-val=0.000

Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value      Exact
--------------------------------------------------
0                    0.2463       0.000
1                   -0.0901       0.253
Combined K-S         0.2463       0.000      0.000

Note: Ties exist in combined dataset;
      there are 22 unique values out of 350 observations.

. 
. cdfplot mx if (trt==0 | trt==3), by(trtment) opt1(lc() lp(solid dash)) xtitl
> e("Share that perceive vendors are honest") ytitle("Cumulative Probability")
>  legend(pos(7) row(1) stack label(1 "Control") label(2 "Combined (PT + MR)")
> )
(0 observations deleted)

. gr export "$output_loc/main_results/te_belief_both_graph.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/main_results/te_belief_both_graph.eps saved as EPS format

. ksmirnov mx if (trt==0 | trt==3), by(trtment) exact //p-val=0.000

Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value      Exact
--------------------------------------------------
0                    0.2800       0.000
1                   -0.0180       0.944
Combined K-S         0.2800       0.000      0.000

Note: Ties exist in combined dataset;
      there are 22 unique values out of 373 observations.

. 
. 
. ** Table 4 -----------------------------------------------------------------
> ----
. ********************************************************************
. **q.2: beliefs update - ability to correctly infer vendor Misconduct - Key r
> eputation ingredient
. sum dhonestVendors* if trtment==0 & dropout_belief==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
dhonestVen~1 |        143    .6853147    .4660227          0          1
dhonestVen~2 |        143    56.25874    28.31207          0        100
dhonestVen~3 |        143    .8461538    .3620694          0          1
dhonestVen~4 |        143    .8811189    .3247862          0          1

. replace fdH0=1 if missing(fdH0) & dropout_belief==0 //NOTE: results (n=792) 
> = results (n=810) if recode implemented to get n=810
(132 real changes made)

. *replace MisconObj=1 if missing(MisconObj) //NOTE: results (n=792) = results
>  (n=810) if recode implemented to get n=810
. reg dhonestVendors1 i.districtID i.c8q3 cfemale cage cmarried cakan cselfemp
> loyed cEducAny cselfIncome c.trtment##c.fdH0 if dropout_belief==0, level(95)
>  r cluster(uniqueLocalityID)

Linear regression                               Number of obs     =        810
                                                F(19, 124)        =       7.56
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2158
                                                Root MSE          =     .43859

                     (Std. err. adjusted for 125 clusters in uniqueLocalityID)
------------------------------------------------------------------------------
             |               Robust
dhonestVen~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |  -.1267238   .0801328    -1.58   0.116     -.285329    .0318815
          3  |   .0607061   .1239367     0.49   0.625    -.1845995    .3060116
          4  |   .0311187   .0738614     0.42   0.674    -.1150736     .177311
          5  |  -.3965805   .1013574    -3.91   0.000    -.5971952   -.1959658
          6  |  -.1999634   .1174611    -1.70   0.091    -.4324519    .0325251
          7  |   .2118908   .0781766     2.71   0.008     .0571573    .3666242
          8  |    .072878   .0752906     0.97   0.335    -.0761431    .2218992
          9  |  -.4202461    .087188    -4.82   0.000    -.5928157   -.2476766
             |
      2.c8q3 |  -.0149948    .042715    -0.35   0.726    -.0995398    .0695501
     cfemale |  -.0102618   .0331096    -0.31   0.757    -.0757949    .0552713
        cage |  -.0002876   .0011712    -0.25   0.806    -.0026058    .0020305
    cmarried |  -.0078552   .0369937    -0.21   0.832    -.0810762    .0653657
       cakan |   .0200969   .0376556     0.53   0.595    -.0544341    .0946279
cselfemplo~d |  -.0079022   .0355313    -0.22   0.824    -.0782286    .0624241
    cEducAny |   .0297944   .0567154     0.53   0.600    -.0824613    .1420502
 cselfIncome |    .012391   .0234054     0.53   0.597    -.0339348    .0587168
     trtment |  -.2824366   .0824946    -3.42   0.001    -.4457166   -.1191566
        fdH0 |  -.1993085   .0878108    -2.27   0.025    -.3731107   -.0255063
             |
   c.trtment#|
      c.fdH0 |   .2729148   .1064786     2.56   0.012     .0621639    .4836657
             |
       _cons |   .9624649    .140447     6.85   0.000     .6844809    1.240449
------------------------------------------------------------------------------

. reg dhonestVendors1 i.districtID i.c8q3 cfemale cage cmarried cakan cselfemp
> loyed cEducAny cselfIncome i.trt##c.fdH0 if dropout_belief==0, level(95) r c
> luster(uniqueLocalityID)

Linear regression                               Number of obs     =        810
                                                F(23, 124)        =       9.82
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2367
                                                Root MSE          =     .43382

                     (Std. err. adjusted for 125 clusters in uniqueLocalityID)
------------------------------------------------------------------------------
             |               Robust
dhonestVen~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |  -.1210708   .0770325    -1.57   0.119    -.2735397    .0313982
          3  |   .0696579   .1325197     0.53   0.600    -.1926357    .3319515
          4  |   .0591028   .0784541     0.75   0.453      -.09618    .2143855
          5  |  -.3771569   .1127096    -3.35   0.001    -.6002407   -.1540732
          6  |  -.2317741   .1085888    -2.13   0.035    -.4467017   -.0168465
          7  |   .2066276   .0812186     2.54   0.012     .0458733    .3673819
          8  |   .0784041   .0735601     1.07   0.289    -.0671919        .224
          9  |  -.4355301   .0796023    -5.47   0.000    -.5930853   -.2779748
             |
      2.c8q3 |  -.0159628   .0421177    -0.38   0.705    -.0993255    .0673999
     cfemale |  -.0073329   .0316953    -0.23   0.817    -.0700666    .0554009
        cage |  -.0002824   .0011611    -0.24   0.808    -.0025806    .0020158
    cmarried |  -.0017948   .0375444    -0.05   0.962    -.0761056    .0725161
       cakan |    .016855   .0347787     0.48   0.629    -.0519817    .0856917
cselfemplo~d |  -.0159091   .0327499    -0.49   0.628    -.0807302    .0489121
    cEducAny |   .0469197   .0554784     0.85   0.399    -.0628875    .1567269
 cselfIncome |   .0117585   .0227082     0.52   0.606    -.0331874    .0567044
             |
         trt |
          1  |  -.3652664   .0871864    -4.19   0.000    -.5378326   -.1927001
          2  |  -.1528984   .0939383    -1.63   0.106    -.3388285    .0330318
          3  |  -.3545883   .0785405    -4.51   0.000    -.5100421   -.1991346
             |
        fdH0 |  -.2168648   .0827851    -2.62   0.010    -.3807197   -.0530099
             |
  trt#c.fdH0 |
          1  |   .3497966   .1226282     2.85   0.005      .107081    .5925122
          2  |   .2351165   .1212912     1.94   0.055    -.0049527    .4751858
          3  |   .2844921   .1095287     2.60   0.011     .0677041    .5012801
             |
       _cons |   .9665045   .1413463     6.84   0.000     .6867406    1.246268
------------------------------------------------------------------------------

. test 1.trt#c.fdH0=3.trt#c.fdH0

 ( 1)  1.trt#c.fdH0 - 3.trt#c.fdH0 = 0

       F(  1,   124) =    0.30
            Prob > F =    0.5867

. test 2.trt#c.fdH0=3.trt#c.fdH0

 ( 1)  2.trt#c.fdH0 - 3.trt#c.fdH0 = 0

       F(  1,   124) =    0.17
            Prob > F =    0.6836

. test 1.trt#c.fdH0=2.trt#c.fdH0

 ( 1)  1.trt#c.fdH0 - 2.trt#c.fdH0 = 0

       F(  1,   124) =    0.76
            Prob > F =    0.3856

. test 1.trt#c.fdH0+2.trt#c.fdH0=3.trt#c.fdH0

 ( 1)  1.trt#c.fdH0 + 2.trt#c.fdH0 - 3.trt#c.fdH0 = 0

       F(  1,   124) =    3.07
            Prob > F =    0.0821

. /*
> *or, easily replicated w Honesty (neg of disHonesty)
> gen hfdH0=1-fdH0
> sum honestVendors* if trtment==0
> reg honestVendors1 i.districtID i.c8q3 cfemale cage cmarried cakan cselfempl
> oyed cEducAny cselfIncome c.trtment##c.hfdH0 if dropout_belief==0, level(90)
>  r cluster(uniqueLocalityID)
> reg honestVendors1 i.districtID i.c8q3 cfemale cage cmarried cakan cselfempl
> oyed cEducAny cselfIncome i.trt##c.hfdH0 if dropout_belief==0, level(90) r c
> luster(uniqueLocalityID)
> */
. 
. ** Table C.3 ---------------------------------------------------------------
> ----
. *Robustness checks - Inference, Multiple Testing, Attrition, LASSO Estimatio
> n
. *POOLED-belief (honesty)
. **************
. *wild cluster bootstrap, pval
. reg honestVendors1 i.districtID i.c8q3 cfemale cage cmarried cakan cselfempl
> oyed cEducAny cselfIncome trtment if dropout_belief==0, r cluster(uniqueLoca
> lityID) level(95)

Linear regression                               Number of obs     =        810
                                                F(17, 124)        =       9.04
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2095
                                                Root MSE          =     .43981

                     (Std. err. adjusted for 125 clusters in uniqueLocalityID)
------------------------------------------------------------------------------
             |               Robust
honestVend~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |   .1257279   .0814709     1.54   0.125    -.0355259    .2869817
          3  |  -.0217072   .1147876    -0.19   0.850     -.248904    .2054897
          4  |   .0114701   .0671406     0.17   0.865    -.1214201    .1443602
          5  |   .4024456     .09846     4.09   0.000     .2075656    .5973256
          6  |   .1858393   .1017827     1.83   0.070    -.0156171    .3872958
          7  |  -.1772037    .069496    -2.55   0.012    -.3147558   -.0396516
          8  |  -.0330669   .0765294    -0.43   0.666      -.18454    .1184062
          9  |   .4439143   .0820224     5.41   0.000      .281569    .6062596
             |
      2.c8q3 |   .0229416    .041212     0.56   0.579    -.0586284    .1045116
     cfemale |   .0079746   .0325037     0.25   0.807    -.0563594    .0723085
        cage |   .0003832   .0011661     0.33   0.743    -.0019248    .0026912
    cmarried |   .0099201    .037732     0.26   0.793    -.0647621    .0846023
       cakan |  -.0141413   .0383773    -0.37   0.713    -.0901008    .0618181
cselfemplo~d |   .0079067   .0362178     0.22   0.828    -.0637785    .0795919
    cEducAny |  -.0337944   .0555408    -0.61   0.544    -.1437252    .0761365
 cselfIncome |   -.018469   .0215949    -0.86   0.394    -.0612113    .0242734
     trtment |    .066966   .0436254     1.54   0.127    -.0193809     .153313
       _cons |   .1918275   .1020865     1.88   0.063    -.0102302    .3938852
------------------------------------------------------------------------------

. boottest trtment, rep($bootstrap_reps) level(95) nogr seed(546)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueLocalityID, Rademacher weights:
  trtment

                          t(124) =     1.5350
                        Prob>|t| =     0.1440

95% confidence set for null hypothesis expression: [−.026, .1642]

. *randomization inf: permuntation test, pval
. preserve

. keep if dropout_belief==0 //ON & OFF
(180 observations deleted)

. ritest trtment _b[trtment], reps($bootstrap_reps) cluster(uniqueLocalityID) 
> strata(districtID) seed(546): reg honestVendors1 i.c8q3 cfemale cage cmarrie
> d cakan cselfemployed cEducAny cselfIncome trtment 
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       810
-------------+----------------------------------   F(9, 800)       =      4.38
       Model |  9.10563206         9   1.0117369   Prob > F        =    0.0000
    Residual |  184.683257       800  .230854071   R-squared       =    0.0470
-------------+----------------------------------   Adj R-squared   =    0.0363
       Total |  193.788889       809  .239541272   Root MSE        =    .48047

------------------------------------------------------------------------------
honestVend~1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      2.c8q3 |   .1636459   .0348961     4.69   0.000     .0951471    .2321446
     cfemale |   .0336439   .0358156     0.94   0.348    -.0366598    .1039476
        cage |   .0005698   .0011888     0.48   0.632    -.0017637    .0029032
    cmarried |   .0091292   .0363348     0.25   0.802    -.0621935     .080452
       cakan |    .063008   .0353707     1.78   0.075    -.0064223    .1324383
cselfemplo~d |  -.0331273   .0386088    -0.86   0.391    -.1089138    .0426592
    cEducAny |   -.095215   .0575073    -1.66   0.098     -.208098     .017668
 cselfIncome |   .0007064   .0221369     0.03   0.975    -.0427468    .0441596
     trtment |   .1029339   .0444763     2.31   0.021     .0156299    .1902378
       _cons |   .2378846   .0992899     2.40   0.017      .042985    .4327842
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000

      Command: regress honestVendors1 i.c8q3 cfemale cage cmarried cakan
                   cselfemployed cEducAny cselfIncome trtment
        _pm_1: _b[trtment]
  res. var(s):  trtment
   Resampling:  Permuting trtment
Clust. var(s):  uniqueLocalityID
     Clusters:  125
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |   .1029339     102    1000  0.1020  0.0096  .0839355   .1224452
------------------------------------------------------------------------------
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. restore

. *mht: implement Romano-Wolf (2005) procedure, pval
. rwolf honestVendors1 dhonestVendors1 if dropout_belief==0, indepvar(trtment 
> trt2 trt3 trt4) reps($bootstrap_reps) seed(124) controls(i.districtID i.c8q3
>  cfemale cage cmarried cakan cselfemployed cEducAny cselfIncome) //family (1
> =beliefs & 2=update)
Bootstrap replications (1000). This may take some time.
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..................................................     50
..................................................     100
..................................................     150
..................................................     200
..................................................     250
..................................................     300
..................................................     350
..................................................     400
..................................................     450
..................................................     500
..................................................     550
..................................................     600
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..................................................     700
..................................................     750
..................................................     800
..................................................     850
..................................................     900
..................................................     950
..................................................     1000




Romano-Wolf step-down adjusted p-values


Independent variable:  trtment
Outcome variables:   honestVendors1 dhonestVendors1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
     honestVendors1 |     0.0073             0.0060              0.0060
    dhonestVendors1 |     0.0073             0.0060              0.0060
------------------------------------------------------------------------------


Independent variable:  trt2
Outcome variables:   honestVendors1 dhonestVendors1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
     honestVendors1 |     0.6350             0.6254              0.6254
    dhonestVendors1 |     0.6350             0.6254              0.6254
------------------------------------------------------------------------------


Independent variable:  trt3
Outcome variables:   honestVendors1 dhonestVendors1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
     honestVendors1 |     0.0001             0.0010              0.0010
    dhonestVendors1 |     0.0001             0.0010              0.0010
------------------------------------------------------------------------------


Independent variable:  trt4
Outcome variables:   honestVendors1 dhonestVendors1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
     honestVendors1 |          .             0.0010              0.0010
    dhonestVendors1 |          .             0.0010              0.0010
------------------------------------------------------------------------------



. *attrition bounds
. leebounds honestVendors1 trtment, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0671
Effect 95% conf. interval          : [-0.0285  0.2083]

------------------------------------------------------------------------------
honestVend~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trtment      |
       lower |   .0569431    .051623     1.10   0.270    -.0442362    .1581224
       upper |   .1288711   .0479531     2.69   0.007     .0348847    .2228576
------------------------------------------------------------------------------

. 
. *SEPARATE-belief (honesty)
. ****************
. *wild cluster bootstrap, pval
. reg honestVendors1 i.districtID i.c8q3 cfemale cage cmarried cakan cselfempl
> oyed cEducAny cselfIncome trt2 trt3 trt4 if dropout_belief==0, r cluster(uni
> queLocalityID) level(95)

Linear regression                               Number of obs     =        810
                                                F(19, 124)        =      10.28
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2284
                                                Root MSE          =     .43506

                     (Std. err. adjusted for 125 clusters in uniqueLocalityID)
------------------------------------------------------------------------------
             |               Robust
honestVend~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |   .1208371   .0772385     1.56   0.120    -.0320396    .2737138
          3  |  -.0226131   .1208142    -0.19   0.852    -.2617383    .2165122
          4  |  -.0033355   .0714978    -0.05   0.963    -.1448496    .1381786
          5  |   .3962387   .1089727     3.64   0.000     .1805512    .6119263
          6  |   .2065302   .0999386     2.07   0.041     .0087236    .4043367
          7  |  -.1771228    .073612    -2.41   0.018    -.3228216    -.031424
          8  |  -.0342844   .0750682    -0.46   0.649    -.1828655    .1142966
          9  |    .454448   .0763689     5.95   0.000     .3032925    .6056034
             |
      2.c8q3 |   .0261275   .0407273     0.64   0.522    -.0544833    .1067383
     cfemale |   .0063692   .0308785     0.21   0.837     -.054748    .0674864
        cage |   .0004249    .001156     0.37   0.714    -.0018632     .002713
    cmarried |    .004266    .038074     0.11   0.911     -.071093    .0796251
       cakan |  -.0137059   .0357539    -0.38   0.702     -.084473    .0570611
cselfemplo~d |   .0119246   .0325696     0.37   0.715    -.0525398    .0763889
    cEducAny |  -.0472899   .0541773    -0.87   0.384     -.154522    .0599422
 cselfIncome |   -.019526    .020722    -0.94   0.348    -.0605407    .0214888
        trt2 |   .1067992    .057862     1.85   0.067     -.007726    .2213243
        trt3 |  -.0450068   .0575898    -0.78   0.436    -.1589932    .0689796
        trt4 |   .1262929   .0543058     2.33   0.022     .0188064    .2337794
       _cons |   .2010583   .1027613     1.96   0.053    -.0023351    .4044517
------------------------------------------------------------------------------

. boottest trt2, rep($bootstrap_reps) level(95) nogr seed(546)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueLocalityID, Rademacher weights:
  trt2

                          t(124) =     1.8458
                        Prob>|t| =     0.0830

95% confidence set for null hypothesis expression: [−.01514, .2308]

. boottest trt3, rep($bootstrap_reps) level(95) nogr seed(546)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueLocalityID, Rademacher weights:
  trt3

                          t(124) =    -0.7815
                        Prob>|t| =     0.4730

95% confidence set for null hypothesis expression: [−.1705, .08063]

. boottest trt4, rep($bootstrap_reps) level(95) nogr seed(546)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueLocalityID, Rademacher weights:
  trt4

                          t(124) =     2.3256
                        Prob>|t| =     0.0270

95% confidence set for null hypothesis expression: [.01496, .2402]

. *randomization inf: permuntation test, pval
. preserve

. keep if dropout_belief==0 //ON & OFF
(180 observations deleted)

. ritest trt2 trt3 trt4 _b[trt2] _b[trt3] _b[trt4], reps($bootstrap_reps) clus
> ter(uniqueLocalityID) strata(districtID) seed(546): reg honestVendors1 i.c8q
> 3 cfemale cage cmarried cakan cselfemployed cEducAny cselfIncome trt2 trt3 t
> rt4 
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       810
-------------+----------------------------------   F(11, 798)      =      4.48
       Model |  11.2790158        11  1.02536507   Prob > F        =    0.0000
    Residual |  182.509873       798  .228709114   R-squared       =    0.0582
-------------+----------------------------------   Adj R-squared   =    0.0452
       Total |  193.788889       809  .239541272   Root MSE        =    .47824

------------------------------------------------------------------------------
honestVend~1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      2.c8q3 |   .1699798   .0348299     4.88   0.000     .1016107    .2383489
     cfemale |    .032391   .0356623     0.91   0.364    -.0376121    .1023941
        cage |   .0005958   .0011838     0.50   0.615    -.0017278    .0029195
    cmarried |   .0046301   .0361994     0.13   0.898    -.0664271    .0756874
       cakan |   .0618701   .0352207     1.76   0.079     -.007266    .1310062
cselfemplo~d |  -.0310608   .0384357    -0.81   0.419    -.1065079    .0443862
    cEducAny |  -.1052716   .0574119    -1.83   0.067    -.2179679    .0074246
 cselfIncome |  -.0007554   .0220418    -0.03   0.973    -.0440222    .0425113
        trt2 |   .1187976   .0510968     2.32   0.020     .0184977    .2190975
        trt3 |   .0221403   .0521685     0.42   0.671    -.0802633    .1245439
        trt4 |   .1614888   .0512755     3.15   0.002     .0608381    .2621395
       _cons |   .2461717   .0990128     2.49   0.013     .0518153     .440528
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000
Warning: 100% of the resampled realizations for _pm_1 are exactly identical to
>  original value
Warning: 100% of the resampled realizations for _pm_2 are exactly identical to
>  original value

      Command: regress honestVendors1 i.c8q3 cfemale cage cmarried cakan
                   cselfemployed cEducAny cselfIncome trt2 trt3 trt4
        _pm_1: trt3
        _pm_2: trt4
        _pm_3: _b[trt2]
        _pm_4: _b[trt3]
        _pm_5: _b[trt4]
  res. var(s):  trt2
   Resampling:  Permuting trt2
Clust. var(s):  uniqueLocalityID
     Clusters:  125
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_2 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_3 |   .1187976      64    1000  0.0640  0.0077  .0496331   .0809951
       _pm_4 |   .0221403     999    1000  0.9990  0.0010  .9944411   .9999747
       _pm_5 |   .1614888       0    1000  0.0000  0.0000         0   .0036821
------------------------------------------------------------------------------
Note: Confidence intervals are with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. restore

. *mht: implement Romano-Wolf (2005) procedure, pval
. rwolf honestVendors1 dhonestVendors1 if dropout_belief==0, indepvar(trt2 trt
> 3 trt4) reps($bootstrap_reps) seed(124) controls(i.districtID i.c8q3 cfemale
>  cage cmarried cakan cselfemployed cEducAny cselfIncome) //family (1=beliefs
>  & 2=update)
Bootstrap replications (1000). This may take some time.
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..................................................     50
..................................................     100
..................................................     150
..................................................     200
..................................................     250
..................................................     300
..................................................     350
..................................................     400
..................................................     450
..................................................     500
..................................................     550
..................................................     600
..................................................     650
..................................................     700
..................................................     750
..................................................     800
..................................................     850
..................................................     900
..................................................     950
..................................................     1000




Romano-Wolf step-down adjusted p-values


Independent variable:  trt2
Outcome variables:   honestVendors1 dhonestVendors1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
     honestVendors1 |     0.0232             0.0220              0.0220
    dhonestVendors1 |     0.0232             0.0220              0.0220
------------------------------------------------------------------------------


Independent variable:  trt3
Outcome variables:   honestVendors1 dhonestVendors1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
     honestVendors1 |     0.3497             0.3506              0.3506
    dhonestVendors1 |     0.3497             0.3506              0.3506
------------------------------------------------------------------------------


Independent variable:  trt4
Outcome variables:   honestVendors1 dhonestVendors1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
     honestVendors1 |     0.0073             0.0060              0.0060
    dhonestVendors1 |     0.0073             0.0060              0.0060
------------------------------------------------------------------------------



. *attrition bounds
. leebounds honestVendors1 trt2, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0447
Effect 95% conf. interval          : [-0.0526  0.1385]

------------------------------------------------------------------------------
honestVend~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt2         |
       lower |   .0210319   .0439597     0.48   0.632    -.0651274    .1071913
       upper |   .0678119   .0421851     1.61   0.108    -.0148694    .1504932
------------------------------------------------------------------------------

. leebounds honestVendors1 trt3, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0209
Effect 95% conf. interval          : [-0.1543  0.0193]

------------------------------------------------------------------------------
honestVend~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt3         |
       lower |  -.0804516   .0416541    -1.93   0.053    -.1620921    .0011889
       upper |   -.059098   .0442147    -1.34   0.181    -.1457572    .0275612
------------------------------------------------------------------------------

. leebounds honestVendors1 trt4, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0252
Effect 95% conf. interval          : [ 0.0010  0.1756]

------------------------------------------------------------------------------
honestVend~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt4         |
       lower |   .0762369   .0431328     1.77   0.077    -.0083018    .1607756
       upper |    .102099   .0421201     2.42   0.015      .019545    .1846529
------------------------------------------------------------------------------

. 
. 
. ** Table C.4 ---------------------------------------------------------------
> ----
. *POOLED-update (dishonesty)
. ****************
. *wild cluster bootstrap, pval
. reg dhonestVendors1 i.districtID i.c8q3 cfemale cage cmarried cakan cselfemp
> loyed cEducAny cselfIncome c.trtment##c.fdH0 if dropout_belief==0, r cluster
> (uniqueLocalityID) level(95)

Linear regression                               Number of obs     =        810
                                                F(19, 124)        =       7.56
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2158
                                                Root MSE          =     .43859

                     (Std. err. adjusted for 125 clusters in uniqueLocalityID)
------------------------------------------------------------------------------
             |               Robust
dhonestVen~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |  -.1267238   .0801328    -1.58   0.116     -.285329    .0318815
          3  |   .0607061   .1239367     0.49   0.625    -.1845995    .3060116
          4  |   .0311187   .0738614     0.42   0.674    -.1150736     .177311
          5  |  -.3965805   .1013574    -3.91   0.000    -.5971952   -.1959658
          6  |  -.1999634   .1174611    -1.70   0.091    -.4324519    .0325251
          7  |   .2118908   .0781766     2.71   0.008     .0571573    .3666242
          8  |    .072878   .0752906     0.97   0.335    -.0761431    .2218992
          9  |  -.4202461    .087188    -4.82   0.000    -.5928157   -.2476766
             |
      2.c8q3 |  -.0149948    .042715    -0.35   0.726    -.0995398    .0695501
     cfemale |  -.0102618   .0331096    -0.31   0.757    -.0757949    .0552713
        cage |  -.0002876   .0011712    -0.25   0.806    -.0026058    .0020305
    cmarried |  -.0078552   .0369937    -0.21   0.832    -.0810762    .0653657
       cakan |   .0200969   .0376556     0.53   0.595    -.0544341    .0946279
cselfemplo~d |  -.0079022   .0355313    -0.22   0.824    -.0782286    .0624241
    cEducAny |   .0297944   .0567154     0.53   0.600    -.0824613    .1420502
 cselfIncome |    .012391   .0234054     0.53   0.597    -.0339348    .0587168
     trtment |  -.2824366   .0824946    -3.42   0.001    -.4457166   -.1191566
        fdH0 |  -.1993085   .0878108    -2.27   0.025    -.3731107   -.0255063
             |
   c.trtment#|
      c.fdH0 |   .2729148   .1064786     2.56   0.012     .0621639    .4836657
             |
       _cons |   .9624649    .140447     6.85   0.000     .6844809    1.240449
------------------------------------------------------------------------------

. *boottest trtment, rep($bootstrap_reps) level(95) nogr seed(546)
. *boottest fdH0, rep($bootstrap_reps) level(95) nogr seed(546)
. boottest c.trtment#c.fdH0, rep($bootstrap_reps) level(95) nogr seed(546)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueLocalityID, Rademacher weights:
  c.trtment#c.fdH0

                          t(124) =     2.5631
                        Prob>|t| =     0.1130

95% confidence set for null hypothesis expression: [−.1363, .5598]

. *randomization inf: permuntation test, pval
. preserve

. keep if dropout_belief==0 //ON & OFF
(180 observations deleted)

. gen trtmentXfdH0= trtment*fdH0  if dropout_belief==0

. ritest trtment trtmentXfdH0 fdH0 _b[trtment] _b[trtmentXfdH0] _b[fdH0], reps
> ($bootstrap_reps) cluster(uniqueLocalityID) strata(districtID) seed(546): re
> g dhonestVendors1 i.districtID i.c8q3 cfemale cage cmarried cakan cselfemplo
> yed cEducAny cselfIncome c.trtment trtmentXfdH0 fdH0
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       810
-------------+----------------------------------   F(19, 790)      =     11.44
       Model |  41.8247789        19  2.20130415   Prob > F        =    0.0000
    Residual |   151.96411       790  .192359633   R-squared       =    0.2158
-------------+----------------------------------   Adj R-squared   =    0.1970
       Total |  193.788889       809  .239541272   Root MSE        =    .43859

------------------------------------------------------------------------------
dhonestVen~1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |  -.1267238   .0768305    -1.65   0.099    -.2775398    .0240922
          3  |   .0607061   .1003878     0.60   0.546    -.1363522    .2577644
          4  |   .0311187   .0654451     0.48   0.635    -.0973481    .1595855
          5  |  -.3965805   .0952874    -4.16   0.000    -.5836269    -.209534
          6  |  -.1999634   .0880354    -2.27   0.023    -.3727744   -.0271525
          7  |   .2118908   .0988138     2.14   0.032     .0179221    .4058594
          8  |    .072878   .0593859     1.23   0.220    -.0436948    .1894509
          9  |  -.4202461   .0516327    -8.14   0.000    -.5215997   -.3188925
             |
      2.c8q3 |  -.0149948   .0354785    -0.42   0.673     -.084638    .0546484
     cfemale |  -.0102618   .0335823    -0.31   0.760     -.076183    .0556594
        cage |  -.0002876   .0011122    -0.26   0.796    -.0024708    .0018956
    cmarried |  -.0078552    .033698    -0.23   0.816    -.0740034     .058293
       cakan |   .0200969    .035527     0.57   0.572    -.0496416    .0898354
cselfemplo~d |  -.0079022   .0358491    -0.22   0.826    -.0782729    .0624685
    cEducAny |   .0297944    .053351     0.56   0.577    -.0749321    .1345209
 cselfIncome |    .012391   .0229798     0.54   0.590    -.0327178    .0574998
     trtment |  -.2824366    .117576    -2.40   0.017    -.5132349   -.0516383
trtmentXfdH0 |   .2729148   .1283149     2.13   0.034     .0210364    .5247932
        fdH0 |  -.1993085   .1245387    -1.60   0.110    -.4437744    .0451574
       _cons |   .9624649   .1586566     6.07   0.000     .6510266    1.273903
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000
Warning: 100% of the resampled realizations for _pm_2 are exactly identical to
>  original value

      Command: regress dhonestVendors1 i.districtID i.c8q3 cfemale cage
                   cmarried cakan cselfemployed cEducAny cselfIncome
                   c.trtment trtmentXfdH0 fdH0
        _pm_1: trtmentXfdH0
        _pm_2: fdH0
        _pm_3: _b[trtment]
        _pm_4: _b[trtmentXfdH0]
        _pm_5: _b[fdH0]
  res. var(s):  trtment
   Resampling:  Permuting trtment
Clust. var(s):  uniqueLocalityID
     Clusters:  125
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_2 |          1    1000    1000  1.0000  0.0000  .9963179          1
       _pm_3 |  -.2824366       0    1000  0.0000  0.0000         0   .0036821
       _pm_4 |   .2729148       0    1000  0.0000  0.0000         0   .0036821
       _pm_5 |  -.1993085       0    1000  0.0000  0.0000         0   .0036821
------------------------------------------------------------------------------
Note: Confidence intervals are with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. restore

. *mht: implement Romano-Wolf (2005) procedure, pval
. gen trtmentXfdH0= trtment*fdH0 if dropout_belief==0
(180 missing values generated)

. rwolf dhonestVendors1 honestVendors1 if dropout_belief==0, indepvar(trtment 
> trt2 trt3 trt4 trtmentXfdH0 fdH0) reps($bootstrap_reps) seed(124) controls(i
> .districtID i.c8q3 cfemale cage cmarried cakan cselfemployed cEducAny cselfI
> ncome) //family (1=beliefs & 2=update)
Bootstrap replications (1000). This may take some time.
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..................................................     50
..................................................     100
..................................................     150
..................................................     200
..................................................     250
..................................................     300
..................................................     350
..................................................     400
..................................................     450
..................................................     500
..................................................     550
..................................................     600
..................................................     650
..................................................     700
..................................................     750
..................................................     800
..................................................     850
..................................................     900
..................................................     950
..................................................     1000




Romano-Wolf step-down adjusted p-values


Independent variable:  trtment
Outcome variables:   dhonestVendors1 honestVendors1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
    dhonestVendors1 |     0.0028             0.0020              0.0020
     honestVendors1 |     0.0028             0.0020              0.0020
------------------------------------------------------------------------------


Independent variable:  trt2
Outcome variables:   dhonestVendors1 honestVendors1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
    dhonestVendors1 |     0.5835             0.5784              0.5784
     honestVendors1 |     0.5835             0.5784              0.5784
------------------------------------------------------------------------------


Independent variable:  trt3
Outcome variables:   dhonestVendors1 honestVendors1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
    dhonestVendors1 |     0.0000             0.0010              0.0010
     honestVendors1 |     0.0000             0.0010              0.0010
------------------------------------------------------------------------------


Independent variable:  trt4
Outcome variables:   dhonestVendors1 honestVendors1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
    dhonestVendors1 |          .             0.0010              0.0010
     honestVendors1 |          .             0.0010              0.0010
------------------------------------------------------------------------------


Independent variable:  trtmentXfdH0
Outcome variables:   dhonestVendors1 honestVendors1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
    dhonestVendors1 |     0.0245             0.0150              0.0150
     honestVendors1 |     0.0245             0.0150              0.0150
------------------------------------------------------------------------------


Independent variable:  fdH0
Outcome variables:   dhonestVendors1 honestVendors1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
    dhonestVendors1 |     0.0827             0.0699              0.0699
     honestVendors1 |     0.0827             0.0699              0.0699
------------------------------------------------------------------------------



. *attrition bounds
. cap leebounds dhonestVendors1 trtmentXfdH0, level(95) cieffect tight() 

. 
. *SEPARATE-update (dishonesty)
. **************
. *wild cluster bootstrap, pval
. reg dhonestVendors1 i.districtID i.c8q3 cfemale cage cmarried cakan cselfemp
> loyed cEducAny cselfIncome c.trt2##c.fdH0 c.trt3##c.fdH0 c.trt4##c.fdH0 if d
> ropout_belief==0, r cluster(uniqueLocalityID) level(95)
note: fdH0 omitted because of collinearity.
note: fdH0 omitted because of collinearity.

Linear regression                               Number of obs     =        810
                                                F(23, 124)        =       9.82
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2367
                                                Root MSE          =     .43382

                     (Std. err. adjusted for 125 clusters in uniqueLocalityID)
------------------------------------------------------------------------------
             |               Robust
dhonestVen~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |  -.1210708   .0770325    -1.57   0.119    -.2735397    .0313982
          3  |   .0696579   .1325197     0.53   0.600    -.1926357    .3319515
          4  |   .0591028   .0784541     0.75   0.453      -.09618    .2143855
          5  |  -.3771569   .1127096    -3.35   0.001    -.6002407   -.1540732
          6  |  -.2317741   .1085888    -2.13   0.035    -.4467017   -.0168465
          7  |   .2066276   .0812186     2.54   0.012     .0458733    .3673819
          8  |   .0784041   .0735601     1.07   0.289    -.0671919        .224
          9  |  -.4355301   .0796023    -5.47   0.000    -.5930853   -.2779748
             |
      2.c8q3 |  -.0159628   .0421177    -0.38   0.705    -.0993255    .0673999
     cfemale |  -.0073329   .0316953    -0.23   0.817    -.0700666    .0554009
        cage |  -.0002824   .0011611    -0.24   0.808    -.0025806    .0020158
    cmarried |  -.0017948   .0375444    -0.05   0.962    -.0761056    .0725161
       cakan |    .016855   .0347787     0.48   0.629    -.0519817    .0856917
cselfemplo~d |  -.0159091   .0327499    -0.49   0.628    -.0807302    .0489121
    cEducAny |   .0469197   .0554784     0.85   0.399    -.0628875    .1567269
 cselfIncome |   .0117585   .0227082     0.52   0.606    -.0331874    .0567044
        trt2 |  -.3652664   .0871864    -4.19   0.000    -.5378326   -.1927001
        fdH0 |  -.2168648   .0827851    -2.62   0.010    -.3807197   -.0530099
             |
      c.trt2#|
      c.fdH0 |   .3497966   .1226282     2.85   0.005      .107081    .5925122
             |
        trt3 |  -.1528984   .0939383    -1.63   0.106    -.3388285    .0330318
        fdH0 |          0  (omitted)
             |
      c.trt3#|
      c.fdH0 |   .2351165   .1212912     1.94   0.055    -.0049527    .4751858
             |
        trt4 |  -.3545883   .0785405    -4.51   0.000    -.5100421   -.1991346
        fdH0 |          0  (omitted)
             |
      c.trt4#|
      c.fdH0 |   .2844921   .1095287     2.60   0.011     .0677041    .5012801
             |
       _cons |   .9665045   .1413463     6.84   0.000     .6867406    1.246268
------------------------------------------------------------------------------

. boottest c.trt2#c.fdH0, rep($bootstrap_reps) level(95) nogr seed(546) //igno
> re rest, not reporting in Table so OK

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueLocalityID, Rademacher weights:
  c.trt2#c.fdH0

                          t(124) =     2.8525
                        Prob>|t| =     0.0480

95% confidence set for null hypothesis expression: [.006899, .7003]

. boottest c.trt3#c.fdH0, rep($bootstrap_reps) level(95) nogr seed(546)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueLocalityID, Rademacher weights:
  c.trt3#c.fdH0

                          t(124) =     1.9384
                        Prob>|t| =     0.1290

95% confidence set for null hypothesis expression: [−.1367, .5856]

. boottest c.trt4#c.fdH0, rep($bootstrap_reps) level(95) nogr seed(546)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueLocalityID, Rademacher weights:
  c.trt4#c.fdH0

                          t(124) =     2.5974
                        Prob>|t| =     0.0830

95% confidence set for null hypothesis expression: [−.07589, .5703]

. *randomization inf: permuntation test, pval
. preserve

. keep if dropout_belief==0 //ON & OFF
(180 observations deleted)

. gen trt2XfdH0= trt2*fdH0 if dropout_belief==0

. gen trt3XfdH0= trt3*fdH0 if dropout_belief==0

. gen trt4XfdH0= trt4*fdH0 if dropout_belief==0

. ritest trt2 trt3 trt4 trt2XfdH0 trt3XfdH0 trt4XfdH0 fdH0 _b[trt2] _b[trt3] _
> b[trt4] _b[trt2XfdH0] _b[trt3XfdH0] _b[trt4XfdH0] _b[fdH0], reps($bootstrap_
> reps) cluster(uniqueLocalityID) strata(districtID) seed(546): reg dhonestVen
> dors1 i.districtID i.c8q3 cfemale cage cmarried cakan cselfemployed cEducAny
>  cselfIncome trt2 trt3 trt4 trt2XfdH0 trt3XfdH0 trt4XfdH0 fdH0
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       810
-------------+----------------------------------   F(23, 786)      =     10.60
       Model |  45.8639332        23  1.99408405   Prob > F        =    0.0000
    Residual |  147.924956       786  .188199689   R-squared       =    0.2367
-------------+----------------------------------   Adj R-squared   =    0.2143
       Total |  193.788889       809  .239541272   Root MSE        =    .43382

------------------------------------------------------------------------------
dhonestVen~1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |  -.1210708   .0760836    -1.59   0.112    -.2704218    .0282803
          3  |   .0696579   .0995626     0.70   0.484    -.1257822    .2650979
          4  |   .0591028   .0657311     0.90   0.369    -.0699264     .188132
          5  |  -.3771569   .0954544    -3.95   0.000    -.5645327   -.1897812
          6  |  -.2317741   .0880054    -2.63   0.009    -.4045276   -.0590207
          7  |   .2066276   .0982353     2.10   0.036      .013793    .3994623
          8  |   .0784041   .0590845     1.33   0.185     -.037578    .1943861
          9  |  -.4355301   .0513086    -8.49   0.000    -.5362481    -.334812
             |
      2.c8q3 |  -.0159628    .035216    -0.45   0.650    -.0850913    .0531657
     cfemale |  -.0073329   .0332654    -0.22   0.826    -.0726324    .0579667
        cage |  -.0002824    .001101    -0.26   0.798    -.0024436    .0018788
    cmarried |  -.0017948   .0334017    -0.05   0.957    -.0673619    .0637724
       cakan |    .016855   .0353173     0.48   0.633    -.0524723    .0861823
cselfemplo~d |  -.0159091   .0356799    -0.45   0.656    -.0859482    .0541301
    cEducAny |   .0469197   .0530138     0.89   0.376    -.0571458    .1509852
 cselfIncome |   .0117585   .0227505     0.52   0.605    -.0329005    .0564175
        trt2 |  -.3652664   .1227234    -2.98   0.003    -.6061708   -.1243619
        trt3 |  -.1528984   .1220228    -1.25   0.211    -.3924274    .0866307
        trt4 |  -.3545883   .1212046    -2.93   0.004    -.5925114   -.1166653
   trt2XfdH0 |   .3497966   .1383024     2.53   0.012     .0783109    .6212823
   trt3XfdH0 |   .2351165   .1368575     1.72   0.086     -.033533    .5037661
   trt4XfdH0 |   .2844921   .1344275     2.12   0.035     .0206127    .5483715
        fdH0 |  -.2168648   .1234021    -1.76   0.079    -.4591015    .0253719
       _cons |   .9665045   .1570864     6.15   0.000     .6581459    1.274863
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000
Warning: 100% of the resampled realizations for _pm_1 are exactly identical to
>  original value
Warning: 100% of the resampled realizations for _pm_2 are exactly identical to
>  original value
Warning: 100% of the resampled realizations for _pm_4 are exactly identical to
>  original value
Warning: 100% of the resampled realizations for _pm_5 are exactly identical to
>  original value
Warning: 100% of the resampled realizations for _pm_6 are exactly identical to
>  original value

      Command: regress dhonestVendors1 i.districtID i.c8q3 cfemale cage
                   cmarried cakan cselfemployed cEducAny cselfIncome trt2
                   trt3 trt4 trt2XfdH0 trt3XfdH0 trt4XfdH0 fdH0
        _pm_1: trt3
        _pm_2: trt4
        _pm_3: trt2XfdH0
        _pm_4: trt3XfdH0
        _pm_5: trt4XfdH0
        _pm_6: fdH0
        _pm_7: _b[trt2]
        _pm_8: _b[trt3]
        _pm_9: _b[trt4]
       _pm_10: _b[trt2XfdH0]
       _pm_11: _b[trt3XfdH0]
       _pm_12: _b[trt4XfdH0]
       _pm_13: _b[fdH0]
  res. var(s):  trt2
   Resampling:  Permuting trt2
Clust. var(s):  uniqueLocalityID
     Clusters:  125
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_2 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_3 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_4 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_5 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_6 |          1    1000    1000  1.0000  0.0000  .9963179          1
       _pm_7 |  -.3652664       0    1000  0.0000  0.0000         0   .0036821
       _pm_8 |  -.1528984     817    1000  0.8170  0.0122  .7916134   .8405042
       _pm_9 |  -.3545883       0    1000  0.0000  0.0000         0   .0036821
      _pm_10 |   .3497966       0    1000  0.0000  0.0000         0   .0036821
      _pm_11 |   .2351165       0    1000  0.0000  0.0000         0   .0036821
      _pm_12 |   .2844921       0    1000  0.0000  0.0000         0   .0036821
      _pm_13 |  -.2168648       0    1000  0.0000  0.0000         0   .0036821
------------------------------------------------------------------------------
Note: Confidence intervals are with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. restore

. *mht: implement Romano-Wolf (2005) procedure, pval
. gen trt2XfdH0= trt2*fdH0 if dropout_belief==0
(180 missing values generated)

. gen trt3XfdH0= trt3*fdH0 if dropout_belief==0
(180 missing values generated)

. gen trt4XfdH0= trt4*fdH0 if dropout_belief==0
(180 missing values generated)

. rwolf dhonestVendors1 honestVendors1 if dropout_belief==0, indepvar(trtment 
> trt2 trt3 trt4 trt2XfdH0 trt3XfdH0 trt4XfdH0 fdH0) reps($bootstrap_reps) see
> d(124) controls(i.districtID i.c8q3 cfemale cage cmarried cakan cselfemploye
> d cEducAny cselfIncome) //family (1=beliefs & 2=update)
Bootstrap replications (1000). This may take some time.
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..................................................     50
..................................................     100
..................................................     150
..................................................     200
..................................................     250
..................................................     300
..................................................     350
..................................................     400
..................................................     450
..................................................     500
..................................................     550
..................................................     600
..................................................     650
..................................................     700
..................................................     750
..................................................     800
..................................................     850
..................................................     900
..................................................     950
..................................................     1000




Romano-Wolf step-down adjusted p-values


Independent variable:  trtment
Outcome variables:   dhonestVendors1 honestVendors1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
    dhonestVendors1 |     0.0035             0.0020              0.0020
     honestVendors1 |     0.0035             0.0020              0.0020
------------------------------------------------------------------------------


Independent variable:  trt2
Outcome variables:   dhonestVendors1 honestVendors1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
    dhonestVendors1 |     0.8637             0.8641              0.8641
     honestVendors1 |     0.8637             0.8641              0.8641
------------------------------------------------------------------------------


Independent variable:  trt3
Outcome variables:   dhonestVendors1 honestVendors1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
    dhonestVendors1 |     0.0018             0.0010              0.0010
     honestVendors1 |     0.0018             0.0010              0.0010
------------------------------------------------------------------------------


Independent variable:  trt4
Outcome variables:   dhonestVendors1 honestVendors1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
    dhonestVendors1 |          .             0.0010              0.0010
     honestVendors1 |          .             0.0010              0.0010
------------------------------------------------------------------------------


Independent variable:  trt2XfdH0
Outcome variables:   dhonestVendors1 honestVendors1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
    dhonestVendors1 |     0.0116             0.0110              0.0110
     honestVendors1 |     0.0116             0.0110              0.0110
------------------------------------------------------------------------------


Independent variable:  trt3XfdH0
Outcome variables:   dhonestVendors1 honestVendors1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
    dhonestVendors1 |     0.0862             0.0709              0.0709
     honestVendors1 |     0.0862             0.0709              0.0709
------------------------------------------------------------------------------


Independent variable:  trt4XfdH0
Outcome variables:   dhonestVendors1 honestVendors1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
    dhonestVendors1 |     0.0346             0.0270              0.0270
     honestVendors1 |     0.0346             0.0270              0.0270
------------------------------------------------------------------------------


Independent variable:  fdH0
Outcome variables:   dhonestVendors1 honestVendors1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
    dhonestVendors1 |     0.0792             0.0689              0.0689
     honestVendors1 |     0.0792             0.0689              0.0689
------------------------------------------------------------------------------



. *attrition bounds
. cap leebounds dhonestVendors1 trt2XfdH0, level(95) cieffect tight() 

. cap leebounds dhonestVendors1 trt2XfdH0, level(95) cieffect tight() 

. cap leebounds dhonestVendors1 trt2XfdH0, level(95) cieffect tight() 

. 
. 
. 
. *Appendix: DIRECT LINK  - directly link belief update induced by treatments 
> with quantities
. ****************************************************************************
> ***************
. gen predictingfd=(dhonestVendors1==fdH1) if (!missing(dhonestVendors1) | !mi
> ssing(fdH1)) //0-1 indicator for matches
(33 missing values generated)

. 
. gen ihs_mmtotamt_t1 = asinh(mmtotamt_t1)
(227 missing values generated)

. gen ihs_mmtotamt_t0 = asinh(mmtotamt_t0)
(65 missing values generated)

. sum ihs_mmtotamt_t0 mmUser_t0 predictingfd if trtment==0 & dropout_belief==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
ihs_mmtota~0 |        141    3.755549    2.578601          0   8.699514
   mmUser_t0 |        141    .9574468    .2025671          0          1
predictingfd |        143    .3916084    .4898256          0          1

. **Effect of consumer belief update (due to trt) on market outcomes? 
. *NOTE: interacted with "trt" so control=0
. 
. *pooled effect
. gen trtmentXpredictingfd= trtment*predictingfd
(33 missing values generated)

. reg ihs_mmtotamt_t1 i.districtID ihs_mmtotamt_t0 cfemale cage cmarried cakan
>  cselfemployed cEducAny cselfIncome i.trtmentXpredictingfd, cluster(loccode)
>  level(95)

Linear regression                               Number of obs     =        723
                                                F(17, 116)        =       5.85
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1146
                                                Root MSE          =     2.3993

                              (Std. err. adjusted for 117 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
ihs_mmtota~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |    -.65297   .3648235    -1.79   0.076    -1.375549    .0696089
          3  |  -.4301119   .5041288    -0.85   0.395    -1.428602    .5683786
          4  |  -.6000231   .3097619    -1.94   0.055    -1.213546    .0134995
          5  |   .5565167   .4192182     1.33   0.187    -.2737978    1.386831
          6  |  -.5051778   .4142256    -1.22   0.225    -1.325604    .3152482
          7  |  -.0839565   .5750611    -0.15   0.884    -1.222938    1.055025
          8  |  -.3919487   .3680926    -1.06   0.289    -1.121002     .337105
          9  |   .8012611    .231555     3.46   0.001     .3426373    1.259885
             |
ihs_mmtota~0 |    .060612   .0435087     1.39   0.166    -.0255624    .1467865
     cfemale |  -.4503861   .2327625    -1.93   0.055    -.9114015    .0106293
        cage |   .0003606   .0057432     0.06   0.950    -.0110144    .0117357
    cmarried |  -.1800222   .2185254    -0.82   0.412    -.6128393    .2527949
       cakan |   .2878485   .2621313     1.10   0.274    -.2313355    .8070326
cselfemplo~d |    .493487   .2282912     2.16   0.033     .0413275    .9456465
    cEducAny |   .5607418   .3371005     1.66   0.099    -.1069282    1.228412
 cselfIncome |   .3455829   .1084334     3.19   0.002     .1308169     .560349
1.trtmentX~d |   .5621407   .1948585     2.88   0.005     .1761988    .9480825
       _cons |    2.91409   .6149084     4.74   0.000     1.696187    4.131994
------------------------------------------------------------------------------

. reg mmUser_t1 i.districtID mmUser_t0 cfemale cage cmarried cakan cselfemploy
> ed cEducAny cselfIncome i.trtmentXpredictingfd, cluster(loccode) level(95)

Linear regression                               Number of obs     =        769
                                                F(17, 122)        =       5.37
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1032
                                                Root MSE          =     .37385

                              (Std. err. adjusted for 123 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
   mmUser_t1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |   -.158667   .0612121    -2.59   0.011    -.2798425   -.0374915
          3  |  -.1667639   .0727257    -2.29   0.024    -.3107317   -.0227961
          4  |  -.1775907   .0547896    -3.24   0.002    -.2860522   -.0691291
          5  |    .019516   .0523919     0.37   0.710    -.0841991    .1232311
          6  |  -.0718026   .0798067    -0.90   0.370    -.2297879    .0861827
          7  |  -.0220334    .083766    -0.26   0.793    -.1878567    .1437898
          8  |  -.0149779   .0574075    -0.26   0.795    -.1286218    .0986661
          9  |   .1009748   .0299976     3.37   0.001     .0415916     .160358
             |
   mmUser_t0 |   .0510431   .0784802     0.65   0.517    -.1043164    .2064025
     cfemale |  -.0510372   .0312004    -1.64   0.104    -.1128015    .0107272
        cage |  -.0004024   .0008483    -0.47   0.636    -.0020816    .0012768
    cmarried |  -.0438875    .030621    -1.43   0.154    -.1045049    .0167298
       cakan |   .0647293   .0383177     1.69   0.094    -.0111244     .140583
cselfemplo~d |   .0590377   .0358933     1.64   0.103    -.0120167    .1300921
    cEducAny |   .1025476   .0511437     2.01   0.047     .0013036    .2037917
 cselfIncome |   .0369976   .0147923     2.50   0.014     .0077148    .0662804
1.trtmentX~d |   .0588547   .0262915     2.24   0.027     .0068081    .1109013
       _cons |   .5968685   .1239662     4.81   0.000     .3514649     .842272
------------------------------------------------------------------------------

. *separate effects
. gen trtXpredictingfd = trt*c.predictingfd
(33 missing values generated)

. reg ihs_mmtotamt_t1 i.districtID ihs_mmtotamt_t0 cfemale cage cmarried cakan
>  cselfemployed cEducAny cselfIncome i.trtXpredictingfd, cluster(loccode) lev
> el(95)

Linear regression                               Number of obs     =        723
                                                F(19, 116)        =       5.84
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1175
                                                Root MSE          =     2.3988

                              (Std. err. adjusted for 117 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
ihs_mmtota~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |  -.7291073    .399089    -1.83   0.070    -1.519553    .0613388
          3  |  -.5031179   .5128855    -0.98   0.329    -1.518952    .5127166
          4  |  -.6121171   .3182937    -1.92   0.057    -1.242538    .0183037
          5  |   .5158678   .4593464     1.12   0.264    -.3939256    1.425661
          6  |  -.5802785   .4150479    -1.40   0.165    -1.402333    .2417762
          7  |  -.1327717   .5675383    -0.23   0.815    -1.256853    .9913095
          8  |  -.4472094   .3660089    -1.22   0.224    -1.172136    .2777173
          9  |    .740575   .2400645     3.08   0.003     .2650969    1.216053
             |
ihs_mmtota~0 |   .0603828    .043807     1.38   0.171    -.0263826    .1471482
     cfemale |  -.4405112   .2308924    -1.91   0.059    -.8978227    .0168003
        cage |   .0004271   .0057468     0.07   0.941    -.0109551    .0118092
    cmarried |  -.1933478   .2193247    -0.88   0.380     -.627748    .2410524
       cakan |   .2995765   .2621809     1.14   0.256    -.2197058    .8188588
cselfemplo~d |   .5045983   .2267763     2.23   0.028     .0554392    .9537574
    cEducAny |   .5694535   .3390323     1.68   0.096    -.1020427     1.24095
 cselfIncome |   .3515397   .1077622     3.26   0.001     .1381031    .5649763
             |
trtXpredic~d |
          1  |   .2823147   .3093161     0.91   0.363    -.3303249    .8949542
          2  |   .7328064   .2879995     2.54   0.012     .1623871    1.303226
          3  |   .7470944   .2594944     2.88   0.005     .2331329    1.261056
             |
       _cons |   2.925001   .6164422     4.74   0.000     1.704059    4.145942
------------------------------------------------------------------------------

. reg mmUser_t1 i.districtID mmUser_t0 cfemale cage cmarried cakan cselfemploy
> ed cEducAny cselfIncome i.trtXpredictingfd, cluster(loccode) level(95)

Linear regression                               Number of obs     =        769
                                                F(19, 122)        =       4.95
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1062
                                                Root MSE          =     .37372

                              (Std. err. adjusted for 123 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
   mmUser_t1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |  -.1699451   .0666015    -2.55   0.012    -.3017894   -.0381009
          3  |  -.1770432   .0743951    -2.38   0.019    -.3243157   -.0297706
          4  |  -.1787009   .0555017    -3.22   0.002    -.2885721   -.0688298
          5  |   .0134604   .0566576     0.24   0.813     -.098699    .1256198
          6  |  -.0817802   .0795132    -1.03   0.306    -.2391845    .0756241
          7  |  -.0287894   .0821255    -0.35   0.727     -.191365    .1337862
          8  |  -.0243907   .0577256    -0.42   0.673    -.1386643    .0898828
          9  |   .0946142   .0296413     3.19   0.002     .0359362    .1532921
             |
   mmUser_t0 |   .0416749   .0789086     0.53   0.598    -.1145326    .1978825
     cfemale |  -.0501316   .0309546    -1.62   0.108    -.1114094    .0111461
        cage |  -.0004148    .000858    -0.48   0.630    -.0021134    .0012837
    cmarried |  -.0463677   .0309732    -1.50   0.137    -.1076822    .0149468
       cakan |    .067418   .0384302     1.75   0.082    -.0086584    .1434944
cselfemplo~d |    .061065   .0358656     1.70   0.091    -.0099344    .1320645
    cEducAny |    .102853   .0515664     1.99   0.048     .0007722    .2049338
 cselfIncome |   .0381304   .0146847     2.60   0.011     .0090606    .0672002
             |
trtXpredic~d |
          1  |   .0144194   .0419013     0.34   0.731    -.0685284    .0973672
          2  |   .0770884   .0343969     2.24   0.027     .0089963    .1451805
          3  |    .092327   .0362138     2.55   0.012     .0206382    .1640159
             |
       _cons |   .6077476    .124962     4.86   0.000     .3603727    .8551224
------------------------------------------------------------------------------

. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
end of do-file

. do "$do_loc/Demand_Mar.19.2023.do" // 5-ish minutes?

. /*
> JPE2023-Annan
> y = demand: usage + savings*
> Title: Phone Surveys + Intensive Tracking: April 2020+
> 
> Input:
>         - FFPhone in 2020/CustomersData.dta
>         - data-Mgt/Stats?/Mkt_census_xtics_+_interventions_localized.dta
>         - data-Mgt/Stats?/ofdrate_mktadminTransactData.dta
> 
> Output:
>         - FFPhone in 2020/_impact-evaluation/te_all_graph.eps
>         - FFPhone in 2020/_impact-evaluation/te_pt_graph.eps
>         - FFPhone in 2020/_impact-evaluation/te_m&r_graph.eps
>         - FFPhone in 2020/_impact-evaluation/te_both_graph.eps
> 
> */
. 
. use "$dta_loc_repl/02_final/Customer_+_Mktcensus_+_Interventions.dta", clear

. 
. gen districtID = ge01

. 
. ** Figure B.4 --------------------------------------------------------------
> ----
. *hist mmtotamt_t1, discrete
. hist mmtotamt_t1
(bin=27, start=0, width=259.25926)

. 
. 
. ** Table B.6 ---------------------------------------------------------------
> ----
. *Attrition - Test for Significance by Treatment Program
. sum dropouts if trt_pool==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    dropouts |        185     .227027     .420047          0          1

. reg dropouts trt_pool, cluster(ge02)

Linear regression                               Number of obs     =        990
                                                F(1, 129)         =       2.06
                                                Prob > F          =     0.1536
                                                R-squared         =     0.0032
                                                Root MSE          =     .38547

                                 (Std. err. adjusted for 130 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
    dropouts | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    trt_pool |  -.0555985   .0387309    -1.44   0.154    -.1322285    .0210316
       _cons |    .227027   .0350939     6.47   0.000     .1575928    .2964612
------------------------------------------------------------------------------

. reg dropouts i.trt, cluster(ge02)

Linear regression                               Number of obs     =        990
                                                F(3, 129)         =       1.05
                                                Prob > F          =     0.3747
                                                R-squared         =     0.0047
                                                Root MSE          =     .38557

                                 (Std. err. adjusted for 130 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
    dropouts | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         trt |
          1  |  -.0726153   .0446738    -1.63   0.107    -.1610034    .0157729
          2  |  -.0324745    .047799    -0.68   0.498     -.127046     .062097
          3  |  -.0603604   .0430674    -1.40   0.163    -.1455702    .0248495
             |
       _cons |    .227027   .0351295     6.46   0.000     .1575224    .2965316
------------------------------------------------------------------------------

. 
. ** Figure B.5 --------------------------------------------------------------
> ----
. *distplot c0a, saving("distplot_ccalls", replace) //customers answer quicker
>  than vendors/business (as expected)
. hist c0a, percent xtitle("Customers: Number of phone call times before answe
> ring survey")
(bin=28, start=1, width=.35714286)

. gr export "$output_loc/main_results/customer_calltimeS.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/main_results/customer_calltimeS.eps saved as EPS format

. 
. **differential attrition/ drop outs?
. tab _merge

   Matching result from |
                  merge |      Freq.     Percent        Cum.
------------------------+-----------------------------------
         Using only (2) |        180       18.18       18.18
            Matched (3) |        810       81.82      100.00
------------------------+-----------------------------------
                  Total |        990      100.00

. bys trtment: sum dropouts 

------------------------------------------------------------------------------
-> trtment = 0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    dropouts |        185     .227027     .420047          0          1

------------------------------------------------------------------------------
-> trtment = 1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    dropouts |        805    .1714286    .3771173          0          1


. dis 0.23-0.18 //control has 5pp higher attrition, responserate for treatment
> =0.82=82% 
.05

. tab dropouts if trtment==0

   dropouts |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        143       77.30       77.30
          1 |         42       22.70      100.00
------------+-----------------------------------
      Total |        185      100.00

. tab dropouts if trtment==1

   dropouts |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        667       82.86       82.86
          1 |        138       17.14      100.00
------------+-----------------------------------
      Total |        805      100.00

. **so trim 0.05/0.82 = 6.1% of treatment group
. **764 responses, so triming 46 customers
. 
. bys trt: sum dropouts 

------------------------------------------------------------------------------
-> trt = 0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    dropouts |        185     .227027     .420047          0          1

------------------------------------------------------------------------------
-> trt = 1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    dropouts |        272    .1544118    .3620091          0          1

------------------------------------------------------------------------------
-> trt = 2

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    dropouts |        257    .1945525    .3966282          0          1

------------------------------------------------------------------------------
-> trt = 3

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    dropouts |        276    .1666667     .373355          0          1


. 
. 
. *********
. *Results*
. *************
. **Effects--graphical: (endline) effects driven by few tails? no
. *ihs transformation 
. **ihs(y) similar, so for our purposes - ihs(y) ~= log(y+1)
. gen ihs_mmtotamt_t1 = asinh(mmtotamt_t1)
(227 missing values generated)

. 
. ** Figure 2 ----------------------------------------------------------------
> ----
. /*
> *(1) voxdev blogpost
> bys trtment: sum ihs_mmtotamt_t1
> quietly eststo Control: mean ihs_mmtotamt_t1 if trtment==0
> quietly eststo Treatment: mean ihs_mmtotamt_t1 if trtment==1
> coefplot Control Treatment, vertical xlabel("") xtitle("{stMono:asinh}(Total
>  Transactions per week)") ytitle(Mean) recast(bar) barwidth(0.25) fcolor(*.5
> ) ciopts(recast(rcap)) citop citype(normal) level(90) graphregion(color(whit
> e)) ylab(3(.5)5,nogrid)
> gr export $dta_loc/_project/_xREPUTATION/slides/results/gr_serviceusage.eps,
>  replace
> gr save $dta_loc/_project/_xREPUTATION/slides/results/gr_serviceusage, repla
> ce
> */
. cdfplot ihs_mmtotamt_t1, by(trtment) opt1(lc() lp(solid dash)) xtitle("{stMo
> no:asinh}(Total Transactions per week)") ytitle("Cummulative Probability") l
> egend(pos(7) row(1) stack label(1 "Control") label(2 "Any treatment"))
(0 observations deleted)

. gr export "$output_loc/main_results/te_all_graph.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/main_results/te_all_graph.eps saved as EPS format

. 
. cdfplot ihs_mmtotamt_t1 if (trt==0 | trt==1), by(trtment) opt1(lc() lp(solid
>  dash)) xtitle("{stMono:asinh}(Total Transactions per week)") ytitle("Cumula
> tive Probability") legend(pos(7) row(1) stack label(1 "Control") label(2 "Tr
> ansparency alone (PT)"))
(0 observations deleted)

. gr export "$output_loc/main_results/te_pt_graph.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/main_results/te_pt_graph.eps saved as EPS format

. 
. cdfplot ihs_mmtotamt_t1 if (trt==0 | trt==2), by(trtment) opt1(lc() lp(solid
>  dash)) xtitle("{stMono:asinh}(Total Transactions per week)") ytitle("Cumula
> tive Probability") legend(pos(7) row(1) stack label(1 "Control") label(2 "Mo
> nitoring alone (MR)"))
(0 observations deleted)

. gr export "$output_loc/main_results/te_m&r_graph.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/main_results/te_m&r_graph.eps saved as EPS format

. 
. cdfplot ihs_mmtotamt_t1 if (trt==0 | trt==3), by(trtment) opt1(lc() lp(solid
>  dash)) xtitle("{stMono:asinh}(Total Transactions per week)") ytitle("Cumula
> tive Probability") legend(pos(7) row(1) stack label(1 "Control") label(2 "Co
> mbined (PT + MR)"))
(0 observations deleted)

. gr export "$output_loc/main_results/te_both_graph.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/main_results/te_both_graph.eps saved as EPS format

. 
. sum ihs_mmtotamt_t1, d

                       ihs_mmtotamt_t1
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                 763
25%     4.094622              0       Sum of wgt.         763

50%     5.298342                      Mean           4.589296
                        Largest       Std. dev.      2.518807
75%     6.396933       8.987197
90%     7.090077       8.987197       Variance       6.344388
95%     7.600903       9.268609       Skewness      -.8743695
99%     8.294049       9.546813       Kurtosis        2.58695

. gen Trim1=ihs_mmtotamt_t1 if ihs_mmtotamt_t1>=r(p5) & ihs_mmtotamt_t1<=r(p95
> )
(253 missing values generated)

. ksmirnov Trim1, by(trtment) exact //p-val=0.091

Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value      Exact
--------------------------------------------------
0                    0.1176       0.050
1                    0.0000       1.000
Combined K-S         0.1176       0.100      0.091

Note: Ties exist in combined dataset;
      there are 45 unique values out of 737 observations.

. ksmirnov Trim1 if (trt==0 | trt==1), by(trtment) exact //p-val=0.481

Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value      Exact
--------------------------------------------------
0                    0.0904       0.263
1                   -0.0094       0.986
Combined K-S         0.0904       0.516      0.481

Note: Ties exist in combined dataset;
      there are 35 unique values out of 347 observations.

. ksmirnov Trim1 if (trt==0 | trt==2), by(trtment) exact //p-val=0.068

Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value      Exact
--------------------------------------------------
0                    0.1455       0.038
1                    0.0000       1.000
Combined K-S         0.1455       0.077      0.068

Note: Ties exist in combined dataset;
      there are 35 unique values out of 317 observations.

. ksmirnov Trim1 if (trt==0 | trt==3), by(trtment) exact //p-val=0.115 or 0,06
> 5

Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value      Exact
--------------------------------------------------
0                    0.1306       0.065
1                    0.0000       1.000
Combined K-S         0.1306       0.129      0.115

Note: Ties exist in combined dataset;
      there are 33 unique values out of 337 observations.

. 
. 
. 
. 
. **Trtment Effects: y=a+bTrtment+FEs+X+e**
. **Mobile Money - (i) $ Transact, (ii) 0/1 Usage, (iii) 0/1 Save, (iv) PCA In
> dex**
. *replace missing obs [y_end, y_base] w their means to maitain same n = 810 (
> reviewer #1, request, very helpful)*
. 
. *1
. *ihs transform--a log allowing for 0 and -ve vals
. sum ihs_mmtotamt_t1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
ihs_mmtota~1 |        763    4.589296    2.518807          0   9.546813

. replace ihs_mmtotamt_t1=r(mean) if missing(ihs_mmtotamt_t1) & _merge==3
(47 real changes made)

. sum mmtotamt_t0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
 mmtotamt_t0 |        925    158.5081    463.5503          0      10000

. replace mmtotamt_t0=r(mean) if missing(mmtotamt_t0) & _merge==3
(41 real changes made)

. gen ihs_mmtotamt_t0 = asinh(mmtotamt_t0)
(24 missing values generated)

. 
. 
. ** Table 5 -----------------------------------------------------------------
> ----
. sum ihs_mmtotamt_t1 if trtment==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
ihs_mmtota~1 |        143    4.096911    2.660754          0   8.294049

. reg ihs_mmtotamt_t1 ihs_mmtotamt_t0 i.districtID cfemale cage cmarried cakan
>  cselfemployed cEducAny cselfIncome trtment, cluster(loccode) level(95)

Linear regression                               Number of obs     =        810
                                                F(17, 124)        =       5.82
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0996
                                                Root MSE          =     2.3444

                              (Std. err. adjusted for 125 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
ihs_mmtota~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
ihs_mmtota~0 |   .0616522   .0408729     1.51   0.134    -.0192467    .1425512
             |
  districtID |
          2  |  -.6499147   .3651041    -1.78   0.078    -1.372558    .0727286
          3  |  -.5748097   .5100945    -1.13   0.262     -1.58443    .4348103
          4  |  -.7133192   .3097631    -2.30   0.023    -1.326427   -.1002113
          5  |   .4922909   .4304336     1.14   0.255    -.3596578     1.34424
          6  |  -.4807949   .4127999    -1.16   0.246    -1.297841    .3362517
          7  |  -.1411792   .5734383    -0.25   0.806    -1.276174    .9938159
          8  |  -.3752144   .3244594    -1.16   0.250     -1.01741    .2669815
          9  |   .7425552   .2339181     3.17   0.002     .2795657    1.205545
             |
     cfemale |   -.467915   .2141258    -2.19   0.031    -.8917299   -.0441001
        cage |   .0001282   .0054775     0.02   0.981    -.0107133    .0109697
    cmarried |  -.0954825   .1919398    -0.50   0.620     -.475385    .2844201
       cakan |   .2721729   .2495359     1.09   0.278    -.2217286    .7660744
cselfemplo~d |   .3863105   .2066464     1.87   0.064    -.0227007    .7953217
    cEducAny |   .3872151   .2783096     1.39   0.167    -.1636376    .9380679
 cselfIncome |   .3224723   .1060746     3.04   0.003      .112521    .5324236
     trtment |   .4587746   .2258517     2.03   0.044     .0117508    .9057984
       _cons |   3.068933   .5823147     5.27   0.000     1.916369    4.221497
------------------------------------------------------------------------------

. reg ihs_mmtotamt_t1 ihs_mmtotamt_t0 i.districtID cfemale cage cmarried cakan
>  cselfemployed cEducAny cselfIncome i.trt, cluster(loccode) level(95)

Linear regression                               Number of obs     =        810
                                                F(19, 124)        =       6.39
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1024
                                                Root MSE          =     2.3437

                              (Std. err. adjusted for 125 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
ihs_mmtota~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
ihs_mmtota~0 |   .0632792   .0410959     1.54   0.126    -.0180611    .1446194
             |
  districtID |
          2  |  -.6665631   .4000151    -1.67   0.098    -1.458305    .1251788
          3  |  -.5587942   .5159237    -1.08   0.281    -1.579952    .4623633
          4  |  -.6724087   .3261955    -2.06   0.041    -1.318041   -.0267765
          5  |   .4768752   .4733421     1.01   0.316    -.4600013    1.413752
          6  |  -.5379254   .4124013    -1.30   0.195    -1.354183    .2783323
          7  |  -.1727532   .5697892    -0.30   0.762    -1.300526    .9550193
          8  |  -.3678935   .3307471    -1.11   0.268    -1.022535    .2867477
          9  |   .7305076   .2360191     3.10   0.002     .2633597    1.197656
             |
     cfemale |   -.460812    .211443    -2.18   0.031    -.8793169    -.042307
        cage |    .000268   .0054092     0.05   0.961    -.0104382    .0109742
    cmarried |  -.0993825   .1925667    -0.52   0.607    -.4805259    .2817609
       cakan |   .2604534   .2484527     1.05   0.297    -.2313041    .7522109
cselfemplo~d |   .3864097   .2059583     1.88   0.063    -.0212395    .7940589
    cEducAny |   .4081132   .2822267     1.45   0.151    -.1504926    .9667189
 cselfIncome |   .3273172   .1060553     3.09   0.003     .1174041    .5372304
             |
         trt |
          1  |   .2621971   .2639521     0.99   0.322    -.2602382    .7846323
          2  |   .5879794   .2687134     2.19   0.031     .0561203    1.119839
          3  |   .5406217   .2553878     2.12   0.036     .0351378    1.046106
             |
       _cons |    3.03988    .586894     5.18   0.000     1.878252    4.201507
------------------------------------------------------------------------------

. test _b[1.trt]=_b[3.trt]

 ( 1)  1.trt - 3.trt = 0

       F(  1,   124) =    1.55
            Prob > F =    0.2152

. test _b[2.trt]=_b[3.trt]

 ( 1)  2.trt - 3.trt = 0

       F(  1,   124) =    0.05
            Prob > F =    0.8323

. test _b[1.trt]=_b[2.trt]

 ( 1)  1.trt - 2.trt = 0

       F(  1,   124) =    1.79
            Prob > F =    0.1832

. test _b[1.trt] + _b[2.trt] =_b[3.trt]

 ( 1)  1.trt + 2.trt - 3.trt = 0

       F(  1,   124) =    0.82
            Prob > F =    0.3675

. 
. *2
. sum mmUser_t0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
   mmUser_t0 |        925    .9437838    .2304634          0          1

. replace mmUser_t0=r(mean) if missing(mmUser_t0) & !missing(mmUser_t1)
(41 real changes made)

. 
. sum mmUser_t1 if trtment==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
   mmUser_t1 |        143    .7342657     .443276          0          1

. reg mmUser_t1 mmUser_t0 i.districtID cfemale cage cmarried cakan cselfemploy
> ed cEducAny cselfIncome trtment, cluster(loccode) level(95)

Linear regression                               Number of obs     =        810
                                                F(17, 124)        =       5.64
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0986
                                                Root MSE          =     .37393

                              (Std. err. adjusted for 125 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
   mmUser_t1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   mmUser_t0 |   .0526295   .0796955     0.66   0.510    -.1051101    .2103692
             |
  districtID |
          2  |  -.1597573   .0593953    -2.69   0.008    -.2773172   -.0421975
          3  |  -.1820805   .0722769    -2.52   0.013    -.3251368   -.0390243
          4  |  -.1867362   .0490384    -3.81   0.000     -.283797   -.0896754
          5  |   .0073177   .0561318     0.13   0.896    -.1037828    .1184182
          6  |  -.0647824    .077592    -0.83   0.405    -.2183587    .0887938
          7  |  -.0294552   .0851419    -0.35   0.730     -.197975    .1390645
          8  |  -.0336201   .0557159    -0.60   0.547    -.1438975    .0766572
          9  |   .0987367   .0298829     3.30   0.001     .0395901    .1578832
             |
     cfemale |  -.0562372   .0305981    -1.84   0.068    -.1167994     .004325
        cage |  -.0005368    .000858    -0.63   0.533     -.002235    .0011613
    cmarried |  -.0377317   .0293129    -1.29   0.200    -.0957501    .0202866
       cakan |   .0624263   .0375636     1.66   0.099    -.0119225    .1367752
cselfemplo~d |   .0552611   .0347694     1.59   0.115    -.0135572    .1240795
    cEducAny |   .0796279   .0471086     1.69   0.093    -.0136132    .1728689
 cselfIncome |   .0369753   .0147966     2.50   0.014     .0076887     .066262
     trtment |   .0732927   .0394449     1.86   0.066    -.0047798    .1513653
       _cons |   .5968428   .1283268     4.65   0.000     .3428482    .8508375
------------------------------------------------------------------------------

. reg mmUser_t1 mmUser_t0 i.districtID cfemale cage cmarried cakan cselfemploy
> ed cEducAny cselfIncome i.trt, cluster(loccode) level(95)

Linear regression                               Number of obs     =        810
                                                F(19, 124)        =       5.35
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1003
                                                Root MSE          =     .37405

                              (Std. err. adjusted for 125 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
   mmUser_t1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   mmUser_t0 |   .0492856   .0804811     0.61   0.541    -.1100091    .2085802
             |
  districtID |
          2  |  -.1624073    .064219    -2.53   0.013    -.2895147   -.0352999
          3  |  -.1795181   .0751888    -2.39   0.018    -.3283379   -.0306983
          4  |  -.1822129   .0509114    -3.58   0.000    -.2829809   -.0814449
          5  |   .0043485   .0602852     0.07   0.943    -.1149729    .1236699
          6  |  -.0709967   .0778783    -0.91   0.364    -.2251396    .0831463
          7  |  -.0339643   .0841901    -0.40   0.687    -.2006002    .1326715
          8  |   -.032962   .0549014    -0.60   0.549    -.1416273    .0757032
          9  |   .0979962   .0299625     3.27   0.001      .038692    .1573003
             |
     cfemale |  -.0553987   .0303009    -1.83   0.070    -.1153727    .0045752
        cage |   -.000517   .0008555    -0.60   0.547    -.0022103    .0011763
    cmarried |  -.0385332   .0295513    -1.30   0.195    -.0970236    .0199572
       cakan |   .0609617   .0373932     1.63   0.106      -.01305    .1349734
cselfemplo~d |   .0555189   .0348353     1.59   0.114    -.0134299    .1244677
    cEducAny |   .0819021   .0475271     1.72   0.087    -.0121674    .1759715
 cselfIncome |   .0376399   .0147269     2.56   0.012     .0084911    .0667886
             |
         trt |
          1  |   .0486146   .0447936     1.09   0.280    -.0400444    .1372736
          2  |    .084436   .0446427     1.89   0.061    -.0039244    .1727964
          3  |   .0879759   .0437271     2.01   0.046     .0014277    .1745241
             |
       _cons |   .5972811   .1296765     4.61   0.000     .3406149    .8539473
------------------------------------------------------------------------------

. test _b[1.trt]=_b[3.trt]

 ( 1)  1.trt - 3.trt = 0

       F(  1,   124) =    1.35
            Prob > F =    0.2480

. test _b[2.trt]=_b[3.trt]

 ( 1)  2.trt - 3.trt = 0

       F(  1,   124) =    0.01
            Prob > F =    0.9173

. test _b[1.trt]=_b[2.trt]

 ( 1)  1.trt - 2.trt = 0

       F(  1,   124) =    1.00
            Prob > F =    0.3190

. test _b[1.trt] + _b[2.trt] =_b[3.trt]

 ( 1)  1.trt + 2.trt - 3.trt = 0

       F(  1,   124) =    0.65
            Prob > F =    0.4207

. 
. *3
. sum save_t0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     save_t0 |        873    .5693013    .4954579          0          1

. replace save_t0=r(mean) if missing(save_t0) & !missing(save_t1)
(79 real changes made)

. 
. sum save_t1 if trtment==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     save_t1 |        143    .6223776    .4864965          0          1

. reg save_t1 save_t0 i.districtID cfemale cage cmarried cakan cselfemployed c
> EducAny cselfIncome trtment, cluster(loccode) level(95)

Linear regression                               Number of obs     =        810
                                                F(17, 124)        =       4.63
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0880
                                                Root MSE          =     .44361

                              (Std. err. adjusted for 125 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
     save_t1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     save_t0 |   .1032858   .0342332     3.02   0.003     .0355288    .1710429
             |
  districtID |
          2  |  -.0627824   .0748605    -0.84   0.403    -.2109522    .0853875
          3  |  -.0527466   .0877934    -0.60   0.549    -.2265144    .1210211
          4  |  -.2019435   .0594674    -3.40   0.001    -.3196461   -.0842409
          5  |   .0112147   .0744649     0.15   0.881    -.1361723    .1586016
          6  |  -.0407936   .0712745    -0.57   0.568    -.1818657    .1002786
          7  |  -.0380573   .1106567    -0.34   0.731    -.2570779    .1809634
          8  |   .0504176   .0494376     1.02   0.310    -.0474331    .1482684
          9  |   .0047595    .047005     0.10   0.920    -.0882766    .0977957
             |
     cfemale |   -.110594   .0342065    -3.23   0.002    -.1782983   -.0428898
        cage |  -.0033688   .0011444    -2.94   0.004    -.0056338   -.0011037
    cmarried |  -.0161894    .037867    -0.43   0.670    -.0911388    .0587599
       cakan |   .1068761   .0380728     2.81   0.006     .0315193    .1822329
cselfemplo~d |   .0281217   .0338561     0.83   0.408     -.038889    .0951325
    cEducAny |   .0232366   .0509398     0.46   0.649    -.0775875    .1240607
 cselfIncome |   .0210328   .0221464     0.95   0.344     -.022801    .0648667
     trtment |   .0751299   .0421204     1.78   0.077    -.0082381     .158498
       _cons |   .6770123   .0833685     8.12   0.000     .5120026     .842022
------------------------------------------------------------------------------

. reg save_t1 save_t0 i.districtID cfemale cage cmarried cakan cselfemployed c
> EducAny cselfIncome i.trt, cluster(loccode) level(95)

Linear regression                               Number of obs     =        810
                                                F(19, 124)        =       5.44
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0945
                                                Root MSE          =     .44258

                              (Std. err. adjusted for 125 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
     save_t1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     save_t0 |   .1009415   .0339867     2.97   0.004     .0336722    .1682108
             |
  districtID |
          2  |  -.0689956   .0806247    -0.86   0.394    -.2285745    .0905833
          3  |  -.0477579   .0812576    -0.59   0.558    -.2085894    .1130736
          4  |  -.1990497    .057115    -3.49   0.001    -.3120963    -.086003
          5  |   .0044202   .0777744     0.06   0.955    -.1495172    .1583576
          6  |  -.0415868    .074361    -0.56   0.577     -.188768    .1055943
          7  |  -.0454431   .1039589    -0.44   0.663    -.2512069    .1603207
          8  |   .0522018   .0452719     1.15   0.251    -.0374041    .1418076
          9  |   .0093773   .0461104     0.20   0.839    -.0818882    .1006427
             |
     cfemale |  -.1100998    .033681    -3.27   0.001     -.176764   -.0434357
        cage |  -.0033185   .0011699    -2.84   0.005    -.0056341    -.001003
    cmarried |  -.0204498   .0381343    -0.54   0.593    -.0959282    .0550286
       cakan |   .1042564   .0366383     2.85   0.005     .0317388    .1767739
cselfemplo~d |   .0306848    .034543     0.89   0.376    -.0376855    .0990552
    cEducAny |   .0209563   .0494752     0.42   0.673     -.076969    .1188816
 cselfIncome |   .0218142   .0222508     0.98   0.329    -.0222264    .0658549
             |
         trt |
          1  |   .0473035   .0475331     1.00   0.322    -.0467778    .1413849
          2  |   .0424749   .0527634     0.81   0.422    -.0619587    .1469085
          3  |   .1313802   .0485096     2.71   0.008     .0353662    .2273943
             |
       _cons |   .6781341   .0835673     8.11   0.000     .5127309    .8435372
------------------------------------------------------------------------------

. test _b[1.trt]=_b[3.trt]

 ( 1)  1.trt - 3.trt = 0

       F(  1,   124) =    3.99
            Prob > F =    0.0480

. test _b[2.trt]=_b[3.trt]

 ( 1)  2.trt - 3.trt = 0

       F(  1,   124) =    3.43
            Prob > F =    0.0663

. test _b[1.trt]=_b[2.trt]

 ( 1)  1.trt - 2.trt = 0

       F(  1,   124) =    0.01
            Prob > F =    0.9191

. test _b[1.trt] + _b[2.trt] =_b[3.trt]

 ( 1)  1.trt + 2.trt - 3.trt = 0

       F(  1,   124) =    0.38
            Prob > F =    0.5368

. 
. *4
. **construct index pooling all directional outcomes ff. Kling et al. (2007)**
. factor ihs_mmtotamt_t1 mmUser_t1 save_t1
(obs=810)

Factor analysis/correlation                      Number of obs    =        810
    Method: principal factors                    Retained factors =          1
    Rotation: (unrotated)                        Number of params =          3

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      1.83469      1.84630            1.0583       1.0583
        Factor2  |     -0.01161      0.07788           -0.0067       1.0516
        Factor3  |     -0.08949            .           -0.0516       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(3)  = 1433.89 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------
        Variable |  Factor1 |   Uniqueness 
    -------------+----------+--------------
    ihs_mmtota~1 |   0.9252 |      0.1441  
       mmUser_t1 |   0.9241 |      0.1461  
         save_t1 |   0.3533 |      0.8752  
    ---------------------------------------

. predict score_MMoneyDd_t1
(option regression assumed; regression scoring)

Scoring coefficients (method = regression)

    ------------------------
        Variable |  Factor1 
    -------------+----------
    ihs_mmtota~1 |  0.48493 
       mmUser_t1 |  0.47579 
         save_t1 |  0.03744 
    ------------------------


. factor ihs_mmtotamt_t0 mmUser_t0 save_t0
(obs=952)

Factor analysis/correlation                      Number of obs    =        952
    Method: principal factors                    Retained factors =          2
    Rotation: (unrotated)                        Number of params =          3

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      0.23735      0.22666            2.9063       2.9063
        Factor2  |      0.01069      0.17707            0.1309       3.0372
        Factor3  |     -0.16638            .           -2.0372       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(3)  =   39.40 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    -------------------------------------------------
        Variable |  Factor1   Factor2 |   Uniqueness 
    -------------+--------------------+--------------
    ihs_mmtota~0 |   0.3485    0.0053 |      0.8786  
       mmUser_t0 |   0.3194   -0.0408 |      0.8963  
         save_t0 |   0.1180    0.0949 |      0.9771  
    -------------------------------------------------

. predict score_MMoneyDd_t0
(option regression assumed; regression scoring)

Scoring coefficients (method = regression)

    ----------------------------------
        Variable |  Factor1   Factor2 
    -------------+--------------------
    ihs_mmtota~0 |  0.29175   0.00566 
       mmUser_t0 |  0.26519  -0.04186 
         save_t0 |  0.09518   0.09442 
    ----------------------------------


. 
. sum score_MMoneyDd_t1 if trtment==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
score_MMon~1 |        143   -.2006386    1.066097  -1.960569   .9871749

. reg score_MMoneyDd_t1 score_MMoneyDd_t0 i.districtID cfemale cage cmarried c
> akan cselfemployed cEducAny cselfIncome trtment, cluster(loccode) level(95)

Linear regression                               Number of obs     =        810
                                                F(17, 124)        =       5.83
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1034
                                                Root MSE          =     .90865

                              (Std. err. adjusted for 125 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
score_MMon~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
score_MMon~0 |   .1104283   .0804994     1.37   0.173    -.0489026    .2697591
             |
  districtID |
          2  |  -.3262963   .1457235    -2.24   0.027     -.614724   -.0378686
          3  |  -.3468483    .185897    -1.87   0.064    -.7147905     .021094
          4  |  -.3847327   .1171759    -3.28   0.001    -.6166567   -.1528087
          5  |   .0978346   .1524106     0.64   0.522    -.2038286    .3994979
          6  |  -.1856478   .1744076    -1.06   0.289    -.5308493    .1595537
          7  |  -.0649726   .2143818    -0.30   0.762    -.4892943    .3593491
          8  |  -.1130361   .1305576    -0.87   0.388    -.3714462    .1453741
          9  |   .2635196   .0804503     3.28   0.001     .1042859    .4227533
             |
     cfemale |  -.1703491   .0791863    -2.15   0.033    -.3270809   -.0136172
        cage |  -.0008074   .0020963    -0.39   0.701    -.0049565    .0033418
    cmarried |  -.0665182   .0724379    -0.92   0.360    -.2098932    .0768568
       cakan |   .1359838    .094552     1.44   0.153    -.0511611    .3231287
cselfemplo~d |   .1461912      .0818     1.79   0.076     -.015714    .3080963
    cEducAny |   .1748436   .1110094     1.58   0.118    -.0448751    .3945623
 cselfIncome |    .109789   .0377634     2.91   0.004     .0350446    .1845334
     trtment |   .1882565   .0911868     2.06   0.041     .0077723    .3687408
       _cons |  -.4517065   .2101579    -2.15   0.034    -.8676679   -.0357452
------------------------------------------------------------------------------

. reg score_MMoneyDd_t1 score_MMoneyDd_t0 i.districtID cfemale cage cmarried c
> akan cselfemployed cEducAny cselfIncome i.trt, cluster(loccode) level(95)

Linear regression                               Number of obs     =        810
                                                F(19, 124)        =       5.86
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1057
                                                Root MSE          =     .90865

                              (Std. err. adjusted for 125 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
score_MMon~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
score_MMon~0 |   .1071308    .081107     1.32   0.189    -.0534028    .2676643
             |
  districtID |
          2  |  -.3327509   .1599425    -2.08   0.040     -.649322   -.0161798
          3  |  -.3394604   .1911879    -1.78   0.078    -.7178748     .038954
          4  |  -.3715539   .1233547    -3.01   0.003    -.6157074   -.1274004
          5  |   .0887691   .1670474     0.53   0.596    -.2418645    .4194026
          6  |  -.2036268   .1749013    -1.16   0.247    -.5498055     .142552
          7  |  -.0763241   .2119112    -0.36   0.719    -.4957557    .3431076
          8  |  -.1112863   .1305637    -0.85   0.396    -.3697084    .1471358
          9  |   .2612342   .0810684     3.22   0.002      .100777    .4216913
             |
     cfemale |  -.1682134   .0782479    -2.15   0.034    -.3230879   -.0133389
        cage |  -.0007615   .0020791    -0.37   0.715    -.0048767    .0033536
    cmarried |  -.0683318    .072853    -0.94   0.350    -.2125283    .0758647
       cakan |   .1318688   .0940721     1.40   0.163    -.0543263    .3180638
cselfemplo~d |   .1468809   .0817339     1.80   0.075    -.0148934    .3086551
    cEducAny |    .181907    .112405     1.62   0.108    -.0405739    .4043878
 cselfIncome |   .1118506   .0377077     2.97   0.004     .0372165    .1864847
             |
         trt |
          1  |   .1188358   .1062341     1.12   0.265    -.0914312    .3291028
          2  |   .2235645    .104546     2.14   0.034     .0166386    .4304904
          3  |    .226321   .1020121     2.22   0.028     .0244105    .4282314
             |
       _cons |   -.460059   .2129497    -2.16   0.033    -.8815461    -.038572
------------------------------------------------------------------------------

. test _b[1.trt]=_b[3.trt]

 ( 1)  1.trt - 3.trt = 0

       F(  1,   124) =    1.58
            Prob > F =    0.2108

. test _b[2.trt]=_b[3.trt]

 ( 1)  2.trt - 3.trt = 0

       F(  1,   124) =    0.00
            Prob > F =    0.9735

. test _b[1.trt]=_b[2.trt]

 ( 1)  1.trt - 2.trt = 0

       F(  1,   124) =    1.35
            Prob > F =    0.2476

. test _b[1.trt] + _b[2.trt] =_b[3.trt]

 ( 1)  1.trt + 2.trt - 3.trt = 0

       F(  1,   124) =    0.76
            Prob > F =    0.3864

. 
. 
. ** Table C.5 ---------------------------------------------------------------
> ----
. *Robustness checks - Inference, Multiple Testing, Attrition, LASSO Estimatio
> n
. *POOLED
. ***wild cluster bootstrap, pval
. reg ihs_mmtotamt_t1 ihs_mmtotamt_t0 i.districtID cfemale cage cmarried cakan
>  cselfemployed cEducAny cselfIncome trtment, cluster(loccode) level(95)

Linear regression                               Number of obs     =        810
                                                F(17, 124)        =       5.82
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0996
                                                Root MSE          =     2.3444

                              (Std. err. adjusted for 125 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
ihs_mmtota~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
ihs_mmtota~0 |   .0616522   .0408729     1.51   0.134    -.0192467    .1425512
             |
  districtID |
          2  |  -.6499147   .3651041    -1.78   0.078    -1.372558    .0727286
          3  |  -.5748097   .5100945    -1.13   0.262     -1.58443    .4348103
          4  |  -.7133192   .3097631    -2.30   0.023    -1.326427   -.1002113
          5  |   .4922909   .4304336     1.14   0.255    -.3596578     1.34424
          6  |  -.4807949   .4127999    -1.16   0.246    -1.297841    .3362517
          7  |  -.1411792   .5734383    -0.25   0.806    -1.276174    .9938159
          8  |  -.3752144   .3244594    -1.16   0.250     -1.01741    .2669815
          9  |   .7425552   .2339181     3.17   0.002     .2795657    1.205545
             |
     cfemale |   -.467915   .2141258    -2.19   0.031    -.8917299   -.0441001
        cage |   .0001282   .0054775     0.02   0.981    -.0107133    .0109697
    cmarried |  -.0954825   .1919398    -0.50   0.620     -.475385    .2844201
       cakan |   .2721729   .2495359     1.09   0.278    -.2217286    .7660744
cselfemplo~d |   .3863105   .2066464     1.87   0.064    -.0227007    .7953217
    cEducAny |   .3872151   .2783096     1.39   0.167    -.1636376    .9380679
 cselfIncome |   .3224723   .1060746     3.04   0.003      .112521    .5324236
     trtment |   .4587746   .2258517     2.03   0.044     .0117508    .9057984
       _cons |   3.068933   .5823147     5.27   0.000     1.916369    4.221497
------------------------------------------------------------------------------

. boottest trt, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by loccode, Rademacher weights:
  trt

                          t(124) =     2.0313
                        Prob>|t| =     0.0550

95% confidence set for null hypothesis expression: [−.009898, .9298]

. reg mmUser_t1 mmUser_t0 i.districtID cfemale cage cmarried cakan cselfemploy
> ed cEducAny cselfIncome trtment, cluster(loccode) level(95)

Linear regression                               Number of obs     =        810
                                                F(17, 124)        =       5.64
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0986
                                                Root MSE          =     .37393

                              (Std. err. adjusted for 125 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
   mmUser_t1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   mmUser_t0 |   .0526295   .0796955     0.66   0.510    -.1051101    .2103692
             |
  districtID |
          2  |  -.1597573   .0593953    -2.69   0.008    -.2773172   -.0421975
          3  |  -.1820805   .0722769    -2.52   0.013    -.3251368   -.0390243
          4  |  -.1867362   .0490384    -3.81   0.000     -.283797   -.0896754
          5  |   .0073177   .0561318     0.13   0.896    -.1037828    .1184182
          6  |  -.0647824    .077592    -0.83   0.405    -.2183587    .0887938
          7  |  -.0294552   .0851419    -0.35   0.730     -.197975    .1390645
          8  |  -.0336201   .0557159    -0.60   0.547    -.1438975    .0766572
          9  |   .0987367   .0298829     3.30   0.001     .0395901    .1578832
             |
     cfemale |  -.0562372   .0305981    -1.84   0.068    -.1167994     .004325
        cage |  -.0005368    .000858    -0.63   0.533     -.002235    .0011613
    cmarried |  -.0377317   .0293129    -1.29   0.200    -.0957501    .0202866
       cakan |   .0624263   .0375636     1.66   0.099    -.0119225    .1367752
cselfemplo~d |   .0552611   .0347694     1.59   0.115    -.0135572    .1240795
    cEducAny |   .0796279   .0471086     1.69   0.093    -.0136132    .1728689
 cselfIncome |   .0369753   .0147966     2.50   0.014     .0076887     .066262
     trtment |   .0732927   .0394449     1.86   0.066    -.0047798    .1513653
       _cons |   .5968428   .1283268     4.65   0.000     .3428482    .8508375
------------------------------------------------------------------------------

. boottest trt, rep($bootstrap_reps) level(95) nogr seed(1546)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by loccode, Rademacher weights:
  trt

                          t(124) =     1.8581
                        Prob>|t| =     0.0920

95% confidence set for null hypothesis expression: [−.01502, .1563]

. reg save_t1 save_t0 i.districtID cfemale cage cmarried cakan cselfemployed c
> EducAny cselfIncome trtment, cluster(loccode) level(95)

Linear regression                               Number of obs     =        810
                                                F(17, 124)        =       4.63
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0880
                                                Root MSE          =     .44361

                              (Std. err. adjusted for 125 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
     save_t1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     save_t0 |   .1032858   .0342332     3.02   0.003     .0355288    .1710429
             |
  districtID |
          2  |  -.0627824   .0748605    -0.84   0.403    -.2109522    .0853875
          3  |  -.0527466   .0877934    -0.60   0.549    -.2265144    .1210211
          4  |  -.2019435   .0594674    -3.40   0.001    -.3196461   -.0842409
          5  |   .0112147   .0744649     0.15   0.881    -.1361723    .1586016
          6  |  -.0407936   .0712745    -0.57   0.568    -.1818657    .1002786
          7  |  -.0380573   .1106567    -0.34   0.731    -.2570779    .1809634
          8  |   .0504176   .0494376     1.02   0.310    -.0474331    .1482684
          9  |   .0047595    .047005     0.10   0.920    -.0882766    .0977957
             |
     cfemale |   -.110594   .0342065    -3.23   0.002    -.1782983   -.0428898
        cage |  -.0033688   .0011444    -2.94   0.004    -.0056338   -.0011037
    cmarried |  -.0161894    .037867    -0.43   0.670    -.0911388    .0587599
       cakan |   .1068761   .0380728     2.81   0.006     .0315193    .1822329
cselfemplo~d |   .0281217   .0338561     0.83   0.408     -.038889    .0951325
    cEducAny |   .0232366   .0509398     0.46   0.649    -.0775875    .1240607
 cselfIncome |   .0210328   .0221464     0.95   0.344     -.022801    .0648667
     trtment |   .0751299   .0421204     1.78   0.077    -.0082381     .158498
       _cons |   .6770123   .0833685     8.12   0.000     .5120026     .842022
------------------------------------------------------------------------------

. boottest trt, rep($bootstrap_reps) level(95) nogr seed(1546)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by loccode, Rademacher weights:
  trt

                          t(124) =     1.7837
                        Prob>|t| =     0.0870

95% confidence set for null hypothesis expression: [−.009126, .1686]

. reg score_MMoneyDd_t1 score_MMoneyDd_t0 i.districtID cfemale cage cmarried c
> akan cselfemployed cEducAny cselfIncome trtment, cluster(loccode) level(95)

Linear regression                               Number of obs     =        810
                                                F(17, 124)        =       5.83
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1034
                                                Root MSE          =     .90865

                              (Std. err. adjusted for 125 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
score_MMon~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
score_MMon~0 |   .1104283   .0804994     1.37   0.173    -.0489026    .2697591
             |
  districtID |
          2  |  -.3262963   .1457235    -2.24   0.027     -.614724   -.0378686
          3  |  -.3468483    .185897    -1.87   0.064    -.7147905     .021094
          4  |  -.3847327   .1171759    -3.28   0.001    -.6166567   -.1528087
          5  |   .0978346   .1524106     0.64   0.522    -.2038286    .3994979
          6  |  -.1856478   .1744076    -1.06   0.289    -.5308493    .1595537
          7  |  -.0649726   .2143818    -0.30   0.762    -.4892943    .3593491
          8  |  -.1130361   .1305576    -0.87   0.388    -.3714462    .1453741
          9  |   .2635196   .0804503     3.28   0.001     .1042859    .4227533
             |
     cfemale |  -.1703491   .0791863    -2.15   0.033    -.3270809   -.0136172
        cage |  -.0008074   .0020963    -0.39   0.701    -.0049565    .0033418
    cmarried |  -.0665182   .0724379    -0.92   0.360    -.2098932    .0768568
       cakan |   .1359838    .094552     1.44   0.153    -.0511611    .3231287
cselfemplo~d |   .1461912      .0818     1.79   0.076     -.015714    .3080963
    cEducAny |   .1748436   .1110094     1.58   0.118    -.0448751    .3945623
 cselfIncome |    .109789   .0377634     2.91   0.004     .0350446    .1845334
     trtment |   .1882565   .0911868     2.06   0.041     .0077723    .3687408
       _cons |  -.4517065   .2101579    -2.15   0.034    -.8676679   -.0357452
------------------------------------------------------------------------------

. boottest trt, rep($bootstrap_reps) level(95) nogr seed(1546)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by loccode, Rademacher weights:
  trt

                          t(124) =     2.0645
                        Prob>|t| =     0.0620

95% confidence set for null hypothesis expression: [−.01386, .3802]

. **randomization inf: permuntation test, pval
. ritest trtment _b[trtment], reps($bootstrap_reps) cluster(loccode) strata(di
> strictID) seed(546): reg ihs_mmtotamt_t1 ihs_mmtotamt_t0 i.districtID cfemal
> e cage cmarried cakan cselfemployed cEducAny cselfIncome trtment
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       810
-------------+----------------------------------   F(17, 792)      =      5.15
       Model |  481.430654        17  28.3194502   Prob > F        =    0.0000
    Residual |  4352.99276       792  5.49620298   R-squared       =    0.0996
-------------+----------------------------------   Adj R-squared   =    0.0803
       Total |  4834.42341       809   5.9758015   Root MSE        =    2.3444

------------------------------------------------------------------------------
ihs_mmtota~1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
ihs_mmtota~0 |   .0616522   .0354012     1.74   0.082    -.0078391    .1311436
             |
  districtID |
          2  |  -.6499147   .4099147    -1.59   0.113    -1.454562    .1547329
          3  |  -.5748097   .5204405    -1.10   0.270    -1.596416    .4467962
          4  |  -.7133192   .3240467    -2.20   0.028    -1.349411   -.0772273
          5  |   .4922909   .4993417     0.99   0.324    -.4878988    1.472481
          6  |  -.4807949   .4128007    -1.16   0.244    -1.291108    .3295179
          7  |  -.1411792   .5191227    -0.27   0.786    -1.160198    .8778398
          8  |  -.3752144   .2926857    -1.28   0.200    -.9497458     .199317
          9  |   .7425552   .2513821     2.95   0.003     .2491013    1.236009
             |
     cfemale |   -.467915   .1770642    -2.64   0.008    -.8154856   -.1203445
        cage |   .0001282   .0059025     0.02   0.983    -.0114581    .0117145
    cmarried |  -.0954825   .1801085    -0.53   0.596     -.449029     .258064
       cakan |   .2721729   .1893152     1.44   0.151    -.0994459    .6437917
cselfemplo~d |   .3863105   .1912384     2.02   0.044     .0109165    .7617045
    cEducAny |   .3872151    .286261     1.35   0.177    -.1747048     .949135
 cselfIncome |   .3224723   .1196375     2.70   0.007     .0876282    .5573164
     trtment |   .4587746    .219115     2.09   0.037     .0286598    .8888894
       _cons |   3.068933   .5181144     5.92   0.000     2.051893    4.085973
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
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..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000

      Command: regress ihs_mmtotamt_t1 ihs_mmtotamt_t0 i.districtID cfemale
                   cage cmarried cakan cselfemployed cEducAny cselfIncome
                   trtment
        _pm_1: _b[trtment]
  res. var(s):  trtment
   Resampling:  Permuting trtment
Clust. var(s):  loccode
     Clusters:  130
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |   .4587746      29    1000  0.0290  0.0053  .0195059   .0413847
------------------------------------------------------------------------------
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. ritest trtment _b[trtment], reps($bootstrap_reps) cluster(loccode) strata(di
> strictID) seed(546): reg mmUser_t1 mmUser_t0 i.districtID cfemale cage cmarr
> ied cakan cselfemployed cEducAny cselfIncome trtment
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       810
-------------+----------------------------------   F(17, 792)      =      5.10
       Model |  12.1127217        17  .712513039   Prob > F        =    0.0000
    Residual |  110.737896       792  .139820575   R-squared       =    0.0986
-------------+----------------------------------   Adj R-squared   =    0.0792
       Total |  122.850617       809  .151854904   Root MSE        =    .37393

------------------------------------------------------------------------------
   mmUser_t1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   mmUser_t0 |   .0526295   .0634797     0.83   0.407    -.0719789     .177238
             |
  districtID |
          2  |  -.1597573   .0654543    -2.44   0.015    -.2882417   -.0312729
          3  |  -.1820805   .0828271    -2.20   0.028    -.3446672   -.0194939
          4  |  -.1867362   .0516833    -3.61   0.000    -.2881886   -.0852838
          5  |   .0073177    .077511     0.09   0.925    -.1448337     .159469
          6  |  -.0647824   .0658868    -0.98   0.326    -.1941159     .064551
          7  |  -.0294552   .0828822    -0.36   0.722    -.1921499    .1332394
          8  |  -.0336201   .0463362    -0.73   0.468    -.1245765    .0573362
          9  |   .0987367   .0399768     2.47   0.014     .0202636    .1772097
             |
     cfemale |  -.0562372   .0281856    -2.00   0.046    -.1115645     -.00091
        cage |  -.0005368   .0009401    -0.57   0.568    -.0023823    .0013086
    cmarried |  -.0377317    .028655    -1.32   0.188    -.0939805     .018517
       cakan |   .0624263   .0302403     2.06   0.039     .0030658    .1217869
cselfemplo~d |   .0552611   .0304899     1.81   0.070    -.0045894    .1151116
    cEducAny |   .0796279   .0454483     1.75   0.080    -.0095855    .1688413
 cselfIncome |   .0369753   .0189777     1.95   0.052    -.0002772    .0742278
     trtment |   .0732927   .0349526     2.10   0.036     .0046822    .1419033
       _cons |   .5968428   .1007014     5.93   0.000     .3991696     .794516
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
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..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000

      Command: regress mmUser_t1 mmUser_t0 i.districtID cfemale cage
                   cmarried cakan cselfemployed cEducAny cselfIncome trtment
        _pm_1: _b[trtment]
  res. var(s):  trtment
   Resampling:  Permuting trtment
Clust. var(s):  loccode
     Clusters:  130
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |   .0732927      35    1000  0.0350  0.0058  .0244975   .0483424
------------------------------------------------------------------------------
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. ritest trtment _b[trtment], reps($bootstrap_reps) cluster(loccode) strata(di
> strictID) seed(546): reg save_t1 save_t0 i.districtID cfemale cage cmarried 
> cakan cselfemployed cEducAny cselfIncome trtment
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       810
-------------+----------------------------------   F(17, 792)      =      4.50
       Model |  15.0379699        17  .884586467   Prob > F        =    0.0000
    Residual |  155.857092       792  .196789257   R-squared       =    0.0880
-------------+----------------------------------   Adj R-squared   =    0.0684
       Total |  170.895062       809  .211242351   Root MSE        =    .44361

------------------------------------------------------------------------------
     save_t1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     save_t0 |   .1032858   .0355012     2.91   0.004     .0335982    .1729735
             |
  districtID |
          2  |  -.0627824   .0778206    -0.81   0.420    -.2155415    .0899767
          3  |  -.0527466   .0984218    -0.54   0.592    -.2459449    .1404517
          4  |  -.2019435   .0617674    -3.27   0.001    -.3231907   -.0806963
          5  |   .0112147   .0938443     0.12   0.905    -.1729982    .1954276
          6  |  -.0407936   .0783758    -0.52   0.603    -.1946424    .1130553
          7  |  -.0380573   .0991279    -0.38   0.701    -.2326418    .1565272
          8  |   .0504176   .0559316     0.90   0.368    -.0593741    .1602094
          9  |   .0047595   .0478394     0.10   0.921    -.0891474    .0986665
             |
     cfemale |   -.110594   .0335424    -3.30   0.001    -.1764366   -.0447515
        cage |  -.0033688   .0011347    -2.97   0.003    -.0055962   -.0011414
    cmarried |  -.0161894    .033999    -0.48   0.634    -.0829283    .0505494
       cakan |   .1068761   .0358441     2.98   0.003     .0365155    .1772367
cselfemplo~d |   .0281217   .0362256     0.78   0.438    -.0429878    .0992313
    cEducAny |   .0232366   .0539136     0.43   0.667    -.0825937     .129067
 cselfIncome |   .0210328   .0226417     0.93   0.353    -.0234121    .0654777
     trtment |   .0751299   .0414761     1.81   0.070    -.0062862    .1565461
       _cons |   .6770123   .0978827     6.92   0.000     .4848721    .8691526
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
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..................................................   950
.................................................. 1,000

      Command: regress save_t1 save_t0 i.districtID cfemale cage cmarried
                   cakan cselfemployed cEducAny cselfIncome trtment
        _pm_1: _b[trtment]
  res. var(s):  trtment
   Resampling:  Permuting trtment
Clust. var(s):  loccode
     Clusters:  130
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |   .0751299      83    1000  0.0830  0.0087  .0666489   .1018533
------------------------------------------------------------------------------
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. ritest trtment _b[trtment], reps($bootstrap_reps) cluster(loccode) strata(di
> strictID) seed(546): reg score_MMoneyDd_t1 score_MMoneyDd_t0 i.districtID cf
> emale cage cmarried cakan cselfemployed cEducAny cselfIncome trtment
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       810
-------------+----------------------------------   F(17, 792)      =      5.37
       Model |  75.4315633        17  4.43715078   Prob > F        =    0.0000
    Residual |  653.912041       792  .825646516   R-squared       =    0.1034
-------------+----------------------------------   Adj R-squared   =    0.0842
       Total |  729.343604       809  .901537211   Root MSE        =    .90865

------------------------------------------------------------------------------
score_MMon~1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
score_MMon~0 |   .1104283   .0721653     1.53   0.126    -.0312295    .2520861
             |
  districtID |
          2  |  -.3262963   .1587103    -2.06   0.040    -.6378389   -.0147537
          3  |  -.3468483   .2017564    -1.72   0.086    -.7428887    .0491922
          4  |  -.3847327   .1255059    -3.07   0.002    -.6310963   -.1383691
          5  |   .0978346   .1888516     0.52   0.605    -.2728742    .4685434
          6  |  -.1856478   .1603455    -1.16   0.247    -.5004002    .1291046
          7  |  -.0649726   .2009178    -0.32   0.746     -.459367    .3294218
          8  |  -.1130361   .1127329    -1.00   0.316    -.3343267    .1082546
          9  |   .2635196   .0975816     2.70   0.007     .0719705    .4550686
             |
     cfemale |  -.1703491   .0685197    -2.49   0.013    -.3048507   -.0358474
        cage |  -.0008074    .002291    -0.35   0.725    -.0053045    .0036898
    cmarried |  -.0665182   .0696969    -0.95   0.340    -.2033306    .0702942
       cakan |   .1359838   .0734769     1.85   0.065    -.0082488    .2802164
cselfemplo~d |   .1461912   .0740976     1.97   0.049     .0007403     .291642
    cEducAny |   .1748436    .110675     1.58   0.115    -.0424074    .3920946
 cselfIncome |    .109789   .0462774     2.37   0.018     .0189482    .2006298
     trtment |   .1882565   .0849149     2.22   0.027     .0215717    .3549413
       _cons |  -.4517065   .1969658    -2.29   0.022    -.8383433   -.0650698
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
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..................................................   450
..................................................   500
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..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000

      Command: regress score_MMoneyDd_t1 score_MMoneyDd_t0 i.districtID
                   cfemale cage cmarried cakan cselfemployed cEducAny
                   cselfIncome trtment
        _pm_1: _b[trtment]
  res. var(s):  trtment
   Resampling:  Permuting trtment
Clust. var(s):  loccode
     Clusters:  130
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |   .1882565      17    1000  0.0170  0.0041  .0099335   .0270795
------------------------------------------------------------------------------
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. **mht: implement Romano-Wolf (2005) procedure, pval
. tab trt if !missing(trt), gen(trt) //gen trts again and verifY

        trt |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        185       18.69       18.69
          1 |        272       27.47       46.16
          2 |        257       25.96       72.12
          3 |        276       27.88      100.00
------------+-----------------------------------
      Total |        990      100.00

. gen trt01 = (trt !=0) if !missing(trt)

. rwolf ihs_mmtotamt_t1 mmUser_t1 save_t1 score_MMoneyDd_t1, indepvar(trtment 
> trt2 trt3 trt4) reps($bootstrap_reps) seed(124) controls(i.districtID cfemal
> e cage cmarried cakan cselfemployed cEducAny cselfIncome) //family (demand: 
> amount, 01 usage, 01 savings)
Bootstrap replications (1000). This may take some time.
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..................................................     50
..................................................     100
..................................................     150
..................................................     200
..................................................     250
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..................................................     950
..................................................     1000




Romano-Wolf step-down adjusted p-values


Independent variable:  trtment
Outcome variables:   ihs_mmtotamt_t1 mmUser_t1 save_t1 score_MMoneyDd_t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
    ihs_mmtotamt_t1 |     0.0272             0.0260              0.0430
          mmUser_t1 |     0.0299             0.0350              0.0430
            save_t1 |     0.0069             0.0150              0.0230
  score_MMoneyDd_t1 |     0.0199             0.0200              0.0340
------------------------------------------------------------------------------


Independent variable:  trt2
Outcome variables:   ihs_mmtotamt_t1 mmUser_t1 save_t1 score_MMoneyDd_t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
    ihs_mmtotamt_t1 |     0.1972             0.1808              0.2468
          mmUser_t1 |     0.2522             0.2448              0.2468
            save_t1 |     0.0288             0.0270              0.0599
  score_MMoneyDd_t1 |     0.1861             0.1818              0.2388
------------------------------------------------------------------------------


Independent variable:  trt3
Outcome variables:   ihs_mmtotamt_t1 mmUser_t1 save_t1 score_MMoneyDd_t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
    ihs_mmtotamt_t1 |     0.9365             0.9281              0.9840
          mmUser_t1 |     0.9251             0.9051              0.9840
            save_t1 |     0.0425             0.0330              0.0829
  score_MMoneyDd_t1 |     0.9304             0.9271              0.9840
------------------------------------------------------------------------------


Independent variable:  trt4
Outcome variables:   ihs_mmtotamt_t1 mmUser_t1 save_t1 score_MMoneyDd_t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
    ihs_mmtotamt_t1 |          .             0.0010              0.0010
          mmUser_t1 |          .             0.0010              0.0010
            save_t1 |          .             0.0010              0.0010
  score_MMoneyDd_t1 |          .             0.0010              0.0010
------------------------------------------------------------------------------



. **attrition bounds
. **1. [Lee Bounds]**
. leebounds ihs_mmtotamt_t1 trtment, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0671
Effect 95% conf. interval          : [-0.0916  1.4722]

------------------------------------------------------------------------------
ihs_mmtota~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trtment      |
       lower |   .3662884   .2778462     1.32   0.187    -.1782802     .910857
       upper |   .9356419   .3255863     2.87   0.004     .2975044    1.573779
------------------------------------------------------------------------------

. leebounds mmUser_t1 trtment, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0671
Effect 95% conf. interval          : [ 0.0162  0.2482]

------------------------------------------------------------------------------
   mmUser_t1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trtment      |
       lower |   .0841333   .0409551     2.05   0.040     .0038627    .1644038
       upper |   .1560613   .0554859     2.81   0.005      .047311    .2648116
------------------------------------------------------------------------------

. leebounds save_t1 trtment, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0671
Effect 95% conf. interval          : [-0.0068  0.2348]

------------------------------------------------------------------------------
     save_t1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trtment      |
       lower |   .0706685   .0467005     1.51   0.130    -.0208629    .1621998
       upper |   .1425965    .055557     2.57   0.010     .0337068    .2514863
------------------------------------------------------------------------------

. leebounds score_MMoneyDd_t1 trtment, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0671
Effect 95% conf. interval          : [-0.0030  0.4755]

------------------------------------------------------------------------------
score_MMon~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trtment      |
       lower |   .1813896   .1038128     1.75   0.081    -.0220798     .384859
       upper |    .243654   .1305704     1.87   0.062    -.0122592    .4995673
------------------------------------------------------------------------------

. **2. [Behajel et al. Bounds]**
. gen attempts= c0a
(180 missing values generated)

. bys trtment: tab attempts

------------------------------------------------------------------------------
-> trtment = 0

   attempts |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        100       69.93       69.93
          2 |         26       18.18       88.11
          3 |          6        4.20       92.31
          4 |          4        2.80       95.10
          5 |          3        2.10       97.20
          6 |          2        1.40       98.60
          9 |          1        0.70       99.30
         11 |          1        0.70      100.00
------------+-----------------------------------
      Total |        143      100.00

------------------------------------------------------------------------------
-> trtment = 1

   attempts |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        454       68.07       68.07
          2 |        119       17.84       85.91
          3 |         63        9.45       95.35
          4 |         13        1.95       97.30
          5 |         14        2.10       99.40
          7 |          2        0.30       99.70
          8 |          2        0.30      100.00
------------+-----------------------------------
      Total |        667      100.00


. **with 3 or less phone /contact attempts: ctr has 92% response rate, trt has
>  95% response rate
. **use number of attempts - "effort" to rank & bound te
. **so trim (95-92)/95 =3% of trt group, x 667= 20 customers out
. **Simply trim as follows:
. foreach x of varlist ihs_mmtotamt_t1 mmUser_t1 save_t1 score_MMoneyDd_t1 {
  2.         preserve
  3.                 display "`x'"
  4.                 gen itemA= `x' if trtment==1 & attempts<=3 
  5.                 egen iranklo_Aa =rank(itemA) if trtment==1, unique //from
>  above
  6.                 egen iranklo_Ab =rank(-itemA) if trtment==1, unique //fro
> m below
  7.                 gen yupperA= `x'
  8.                 replace yupperA=. if (trtment==1 & iranklo_Aa<=20) | (trt
> ment==1 & attempts>3) //trim differences within 3 attempts and cut off all a
> bove 3-attempts
  9.                 gen ylowerA= `x'
 10.                 replace ylowerA=. if (trtment==1 & iranklo_Ab<=20) | (trt
> ment==1 & attempts>3)
 11.                 reg ylowerA  trtment, r
 12.                 reg yupperA trtment, r
 13.         restore
 14. } 
ihs_mmtotamt_t1
(354 missing values generated)
(354 missing values generated)
(354 missing values generated)
(180 missing values generated)
(51 real changes made, 51 to missing)
(180 missing values generated)
(51 real changes made, 51 to missing)

Linear regression                               Number of obs     =        759
                                                F(1, 757)         =       4.12
                                                Prob > F          =     0.0428
                                                R-squared         =     0.0063
                                                Root MSE          =     2.4002

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     trtment |   .4893194   .2411599     2.03   0.043     .0158977    .9627411
       _cons |   4.096911   .2220168    18.45   0.000     3.661069    4.532752
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        759
                                                F(1, 757)         =       9.93
                                                Prob > F          =     0.0017
                                                R-squared         =     0.0157
                                                Root MSE          =     2.3467

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     trtment |   .7567573   .2401055     3.15   0.002     .2854055    1.228109
       _cons |   4.096911   .2220168    18.45   0.000     3.661069    4.532752
------------------------------------------------------------------------------
mmUser_t1
(354 missing values generated)
(354 missing values generated)
(354 missing values generated)
(180 missing values generated)
(51 real changes made, 51 to missing)
(180 missing values generated)
(51 real changes made, 51 to missing)

Linear regression                               Number of obs     =        759
                                                F(1, 757)         =       5.10
                                                Prob > F          =     0.0242
                                                R-squared         =     0.0080
                                                Root MSE          =     .39308

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     trtment |   .0904096   .0400427     2.26   0.024     .0118017    .1690175
       _cons |   .7342657   .0369875    19.85   0.000     .6616554    .8068761
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        759
                                                F(1, 757)         =       9.63
                                                Prob > F          =     0.0020
                                                R-squared         =     0.0167
                                                Root MSE          =     .36946

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     trtment |   .1228771   .0395902     3.10   0.002     .0451575    .2005967
       _cons |   .7342657   .0369875    19.85   0.000     .6616554    .8068761
------------------------------------------------------------------------------
save_t1
(354 missing values generated)
(354 missing values generated)
(354 missing values generated)
(180 missing values generated)
(51 real changes made, 51 to missing)
(180 missing values generated)
(51 real changes made, 51 to missing)

Linear regression                               Number of obs     =        759
                                                F(1, 757)         =       3.68
                                                Prob > F          =     0.0556
                                                R-squared         =     0.0052
                                                Root MSE          =     .46119

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     trtment |   .0854146   .0445478     1.92   0.056    -.0020372    .1728664
       _cons |   .6223776   .0405939    15.33   0.000     .5426876    .7020676
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        759
                                                F(1, 757)         =       7.09
                                                Prob > F          =     0.0079
                                                R-squared         =     0.0105
                                                Root MSE          =     .44817

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     trtment |   .1178821   .0442812     2.66   0.008     .0309536    .2048107
       _cons |   .6223776   .0405939    15.33   0.000     .5426876    .7020676
------------------------------------------------------------------------------
score_MMoneyDd_t1
(354 missing values generated)
(354 missing values generated)
(354 missing values generated)
(180 missing values generated)
(51 real changes made, 51 to missing)
(180 missing values generated)
(51 real changes made, 51 to missing)

Linear regression                               Number of obs     =        759
                                                F(1, 757)         =       4.96
                                                Prob > F          =     0.0262
                                                R-squared         =     0.0078
                                                Root MSE          =     .94772

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     trtment |   .2146627   .0963502     2.23   0.026     .0255173     .403808
       _cons |  -.2006386   .0889565    -2.26   0.024    -.3752694   -.0260077
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        759
                                                F(1, 757)         =      10.53
                                                Prob > F          =     0.0012
                                                R-squared         =     0.0178
                                                Root MSE          =      .9012

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     trtment |   .3097521   .0954533     3.25   0.001     .1223674    .4971367
       _cons |  -.2006386   .0889565    -2.26   0.024    -.3752694   -.0260077
------------------------------------------------------------------------------

. *
. 
. ** Table C.6 ---------------------------------------------------------------
> ----
. *SEPARATE
. ***wild cluster bootstrap, pval
. reg ihs_mmtotamt_t1 ihs_mmtotamt_t0 i.districtID cfemale cage cmarried cakan
>  cselfemployed cEducAny cselfIncome trt2 trt3 trt4, cluster(loccode) level(9
> 5)

Linear regression                               Number of obs     =        810
                                                F(19, 124)        =       6.39
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1024
                                                Root MSE          =     2.3437

                              (Std. err. adjusted for 125 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
ihs_mmtota~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
ihs_mmtota~0 |   .0632792   .0410959     1.54   0.126    -.0180611    .1446194
             |
  districtID |
          2  |  -.6665631   .4000151    -1.67   0.098    -1.458305    .1251788
          3  |  -.5587942   .5159237    -1.08   0.281    -1.579952    .4623633
          4  |  -.6724087   .3261955    -2.06   0.041    -1.318041   -.0267765
          5  |   .4768752   .4733421     1.01   0.316    -.4600013    1.413752
          6  |  -.5379254   .4124013    -1.30   0.195    -1.354183    .2783323
          7  |  -.1727532   .5697892    -0.30   0.762    -1.300526    .9550193
          8  |  -.3678935   .3307471    -1.11   0.268    -1.022535    .2867477
          9  |   .7305076   .2360191     3.10   0.002     .2633597    1.197656
             |
     cfemale |   -.460812    .211443    -2.18   0.031    -.8793169    -.042307
        cage |    .000268   .0054092     0.05   0.961    -.0104382    .0109742
    cmarried |  -.0993825   .1925667    -0.52   0.607    -.4805259    .2817609
       cakan |   .2604534   .2484527     1.05   0.297    -.2313041    .7522109
cselfemplo~d |   .3864097   .2059583     1.88   0.063    -.0212395    .7940589
    cEducAny |   .4081132   .2822267     1.45   0.151    -.1504926    .9667189
 cselfIncome |   .3273172   .1060553     3.09   0.003     .1174041    .5372304
        trt2 |   .2621971   .2639521     0.99   0.322    -.2602382    .7846323
        trt3 |   .5879794   .2687134     2.19   0.031     .0561203    1.119839
        trt4 |   .5406217   .2553878     2.12   0.036     .0351378    1.046106
       _cons |    3.03988    .586894     5.18   0.000     1.878252    4.201507
------------------------------------------------------------------------------

. boottest trt2, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by loccode, Rademacher weights:
  trt2

                          t(124) =     0.9934
                        Prob>|t| =     0.3510

95% confidence set for null hypothesis expression: [−.3268, .7984]

. boottest trt3, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by loccode, Rademacher weights:
  trt3

                          t(124) =     2.1881
                        Prob>|t| =     0.0400

95% confidence set for null hypothesis expression: [.04371, 1.154]

. boottest trt4, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by loccode, Rademacher weights:
  trt4

                          t(124) =     2.1169
                        Prob>|t| =     0.0420

95% confidence set for null hypothesis expression: [.01977, 1.069]

. reg mmUser_t1 mmUser_t0 i.districtID cfemale cage cmarried cakan cselfemploy
> ed cEducAny cselfIncome trt2 trt3 trt4, cluster(loccode) level(95)

Linear regression                               Number of obs     =        810
                                                F(19, 124)        =       5.35
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1003
                                                Root MSE          =     .37405

                              (Std. err. adjusted for 125 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
   mmUser_t1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   mmUser_t0 |   .0492856   .0804811     0.61   0.541    -.1100091    .2085802
             |
  districtID |
          2  |  -.1624073    .064219    -2.53   0.013    -.2895147   -.0352999
          3  |  -.1795181   .0751888    -2.39   0.018    -.3283379   -.0306983
          4  |  -.1822129   .0509114    -3.58   0.000    -.2829809   -.0814449
          5  |   .0043485   .0602852     0.07   0.943    -.1149729    .1236699
          6  |  -.0709967   .0778783    -0.91   0.364    -.2251396    .0831463
          7  |  -.0339643   .0841901    -0.40   0.687    -.2006002    .1326715
          8  |   -.032962   .0549014    -0.60   0.549    -.1416273    .0757032
          9  |   .0979962   .0299625     3.27   0.001      .038692    .1573003
             |
     cfemale |  -.0553987   .0303009    -1.83   0.070    -.1153727    .0045752
        cage |   -.000517   .0008555    -0.60   0.547    -.0022103    .0011763
    cmarried |  -.0385332   .0295513    -1.30   0.195    -.0970236    .0199572
       cakan |   .0609617   .0373932     1.63   0.106      -.01305    .1349734
cselfemplo~d |   .0555189   .0348353     1.59   0.114    -.0134299    .1244677
    cEducAny |   .0819021   .0475271     1.72   0.087    -.0121674    .1759715
 cselfIncome |   .0376399   .0147269     2.56   0.012     .0084911    .0667886
        trt2 |   .0486146   .0447936     1.09   0.280    -.0400444    .1372736
        trt3 |    .084436   .0446427     1.89   0.061    -.0039244    .1727964
        trt4 |   .0879759   .0437271     2.01   0.046     .0014277    .1745241
       _cons |   .5972811   .1296765     4.61   0.000     .3406149    .8539473
------------------------------------------------------------------------------

. boottest trt2, rep($bootstrap_reps) level(95) nogr seed(1546)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by loccode, Rademacher weights:
  trt2

                          t(124) =     1.0853
                        Prob>|t| =     0.3080

95% confidence set for null hypothesis expression: [−.05255, .1382]

. boottest trt3, rep($bootstrap_reps) level(95) nogr seed(1546)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by loccode, Rademacher weights:
  trt3

                          t(124) =     1.8914
                        Prob>|t| =     0.0730

95% confidence set for null hypothesis expression: [−.007855, .1772]

. boottest trt4, rep($bootstrap_reps) level(95) nogr seed(1546)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by loccode, Rademacher weights:
  trt4

                          t(124) =     2.0119
                        Prob>|t| =     0.0610

95% confidence set for null hypothesis expression: [−.005576, .1777]

. reg save_t1 save_t0 i.districtID cfemale cage cmarried cakan cselfemployed c
> EducAny cselfIncome trt2 trt3 trt4, cluster(loccode) level(95)

Linear regression                               Number of obs     =        810
                                                F(19, 124)        =       5.44
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0945
                                                Root MSE          =     .44258

                              (Std. err. adjusted for 125 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
     save_t1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     save_t0 |   .1009415   .0339867     2.97   0.004     .0336722    .1682108
             |
  districtID |
          2  |  -.0689956   .0806247    -0.86   0.394    -.2285745    .0905833
          3  |  -.0477579   .0812576    -0.59   0.558    -.2085894    .1130736
          4  |  -.1990497    .057115    -3.49   0.001    -.3120963    -.086003
          5  |   .0044202   .0777744     0.06   0.955    -.1495172    .1583576
          6  |  -.0415868    .074361    -0.56   0.577     -.188768    .1055943
          7  |  -.0454431   .1039589    -0.44   0.663    -.2512069    .1603207
          8  |   .0522018   .0452719     1.15   0.251    -.0374041    .1418076
          9  |   .0093773   .0461104     0.20   0.839    -.0818882    .1006427
             |
     cfemale |  -.1100998    .033681    -3.27   0.001     -.176764   -.0434357
        cage |  -.0033185   .0011699    -2.84   0.005    -.0056341    -.001003
    cmarried |  -.0204498   .0381343    -0.54   0.593    -.0959282    .0550286
       cakan |   .1042564   .0366383     2.85   0.005     .0317388    .1767739
cselfemplo~d |   .0306848    .034543     0.89   0.376    -.0376855    .0990552
    cEducAny |   .0209563   .0494752     0.42   0.673     -.076969    .1188816
 cselfIncome |   .0218142   .0222508     0.98   0.329    -.0222264    .0658549
        trt2 |   .0473035   .0475331     1.00   0.322    -.0467778    .1413849
        trt3 |   .0424749   .0527634     0.81   0.422    -.0619587    .1469085
        trt4 |   .1313802   .0485096     2.71   0.008     .0353662    .2273943
       _cons |   .6781341   .0835673     8.11   0.000     .5127309    .8435372
------------------------------------------------------------------------------

. boottest trt2, rep($bootstrap_reps) level(95) nogr seed(1546)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by loccode, Rademacher weights:
  trt2

                          t(124) =     0.9952
                        Prob>|t| =     0.3520

95% confidence set for null hypothesis expression: [−.05039, .1462]

. boottest trt3, rep($bootstrap_reps) level(95) nogr seed(1546)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by loccode, Rademacher weights:
  trt3

                          t(124) =     0.8050
                        Prob>|t| =     0.4390

95% confidence set for null hypothesis expression: [−.06558, .1566]

. boottest trt4, rep($bootstrap_reps) level(95) nogr seed(1546)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by loccode, Rademacher weights:
  trt4

                          t(124) =     2.7083
                        Prob>|t| =     0.0090

95% confidence set for null hypothesis expression: [.03184, .2368]

. reg score_MMoneyDd_t1 score_MMoneyDd_t0 i.districtID cfemale cage cmarried c
> akan cselfemployed cEducAny cselfIncome trt2 trt3 trt4, cluster(loccode) lev
> el(95)

Linear regression                               Number of obs     =        810
                                                F(19, 124)        =       5.86
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1057
                                                Root MSE          =     .90865

                              (Std. err. adjusted for 125 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
score_MMon~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
score_MMon~0 |   .1071308    .081107     1.32   0.189    -.0534028    .2676643
             |
  districtID |
          2  |  -.3327509   .1599425    -2.08   0.040     -.649322   -.0161798
          3  |  -.3394604   .1911879    -1.78   0.078    -.7178748     .038954
          4  |  -.3715539   .1233547    -3.01   0.003    -.6157074   -.1274004
          5  |   .0887691   .1670474     0.53   0.596    -.2418645    .4194026
          6  |  -.2036268   .1749013    -1.16   0.247    -.5498055     .142552
          7  |  -.0763241   .2119112    -0.36   0.719    -.4957557    .3431076
          8  |  -.1112863   .1305637    -0.85   0.396    -.3697084    .1471358
          9  |   .2612342   .0810684     3.22   0.002      .100777    .4216913
             |
     cfemale |  -.1682134   .0782479    -2.15   0.034    -.3230879   -.0133389
        cage |  -.0007615   .0020791    -0.37   0.715    -.0048767    .0033536
    cmarried |  -.0683318    .072853    -0.94   0.350    -.2125283    .0758647
       cakan |   .1318688   .0940721     1.40   0.163    -.0543263    .3180638
cselfemplo~d |   .1468809   .0817339     1.80   0.075    -.0148934    .3086551
    cEducAny |    .181907    .112405     1.62   0.108    -.0405739    .4043878
 cselfIncome |   .1118506   .0377077     2.97   0.004     .0372165    .1864847
        trt2 |   .1188358   .1062341     1.12   0.265    -.0914312    .3291028
        trt3 |   .2235645    .104546     2.14   0.034     .0166386    .4304904
        trt4 |    .226321   .1020121     2.22   0.028     .0244105    .4282314
       _cons |   -.460059   .2129497    -2.16   0.033    -.8815461    -.038572
------------------------------------------------------------------------------

. boottest trt2, rep($bootstrap_reps) level(95) nogr seed(1546)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by loccode, Rademacher weights:
  trt2

                          t(124) =     1.1186
                        Prob>|t| =     0.2920

95% confidence set for null hypothesis expression: [−.1151, .3416]

. boottest trt3, rep($bootstrap_reps) level(95) nogr seed(1546)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by loccode, Rademacher weights:
  trt3

                          t(124) =     2.1384
                        Prob>|t| =     0.0420

95% confidence set for null hypothesis expression: [.007583, .4377]

. boottest trt4, rep($bootstrap_reps) level(95) nogr seed(1546)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by loccode, Rademacher weights:
  trt4

                          t(124) =     2.2186
                        Prob>|t| =     0.0350

95% confidence set for null hypothesis expression: [.01675, .432]

. **randomization inf: permuntation test, pval
. ritest trt2 trt3 trt4 _b[trt2] _b[trt3] _b[trt4], reps($bootstrap_reps) clus
> ter(loccode) strata(districtID) seed(546): reg ihs_mmtotamt_t1 ihs_mmtotamt_
> t0 i.districtID cfemale cage cmarried cakan cselfemployed cEducAny cselfInco
> me trt2 trt3 trt4
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       810
-------------+----------------------------------   F(19, 790)      =      4.74
       Model |  494.866327        19  26.0455961   Prob > F        =    0.0000
    Residual |  4339.55709       790  5.49311024   R-squared       =    0.1024
-------------+----------------------------------   Adj R-squared   =    0.0808
       Total |  4834.42341       809   5.9758015   Root MSE        =    2.3437

------------------------------------------------------------------------------
ihs_mmtota~1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
ihs_mmtota~0 |   .0632792    .035491     1.78   0.075    -.0063886     .132947
             |
  districtID |
          2  |  -.6665631   .4100774    -1.63   0.104    -1.471533    .1384071
          3  |  -.5587942   .5204732    -1.07   0.283    -1.580468    .4628798
          4  |  -.6724087   .3250649    -2.07   0.039    -1.310502   -.0343157
          5  |   .4768752   .4997835     0.95   0.340    -.5041854    1.457936
          6  |  -.5379254   .4144872    -1.30   0.195    -1.351552    .2757011
          7  |  -.1727532   .5194703    -0.33   0.740    -1.192458     .846952
          8  |  -.3678935   .2926439    -1.26   0.209    -.9423452    .2065582
          9  |   .7305076   .2518381     2.90   0.004     .2361566    1.224859
             |
     cfemale |   -.460812   .1770748    -2.60   0.009    -.8084047   -.1132192
        cage |    .000268   .0059023     0.05   0.964    -.0113181    .0118541
    cmarried |  -.0993825   .1802789    -0.55   0.582    -.4532648    .2544998
       cakan |   .2604534    .189439     1.37   0.170      -.11141    .6323168
cselfemplo~d |   .3864097   .1912835     2.02   0.044     .0109257    .7618936
    cEducAny |   .4081132   .2866406     1.42   0.155    -.1545541    .9707804
 cselfIncome |   .3273172   .1196479     2.74   0.006     .0924518    .5621827
        trt2 |   .2621971   .2530614     1.04   0.300    -.2345553    .7589494
        trt3 |   .5879794   .2590245     2.27   0.023     .0795218    1.096437
        trt4 |   .5406217   .2526015     2.14   0.033     .0447722    1.036471
       _cons |    3.03988   .5184669     5.86   0.000     2.022144    4.057615
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000
Warning: 100% of the resampled realizations for _pm_1 are exactly identical to
>  original value
Warning: 100% of the resampled realizations for _pm_2 are exactly identical to
>  original value

      Command: regress ihs_mmtotamt_t1 ihs_mmtotamt_t0 i.districtID cfemale
                   cage cmarried cakan cselfemployed cEducAny cselfIncome
                   trt2 trt3 trt4
        _pm_1: trt3
        _pm_2: trt4
        _pm_3: _b[trt2]
        _pm_4: _b[trt3]
        _pm_5: _b[trt4]
  res. var(s):  trt2
   Resampling:  Permuting trt2
Clust. var(s):  loccode
     Clusters:  130
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_2 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_3 |   .2621971     240    1000  0.2400  0.0135  .2138268   .2677134
       _pm_4 |   .5879794       0    1000  0.0000  0.0000         0   .0036821
       _pm_5 |   .5406217       0    1000  0.0000  0.0000         0   .0036821
------------------------------------------------------------------------------
Note: Confidence intervals are with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. ritest trt2 trt3 trt4 _b[trt2] _b[trt3] _b[trt4], reps($bootstrap_reps) clus
> ter(loccode) strata(districtID) seed(546): reg mmUser_t1 mmUser_t0 i.distric
> tID cfemale cage cmarried cakan cselfemployed cEducAny cselfIncome trt2 trt3
>  trt4
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       810
-------------+----------------------------------   F(19, 790)      =      4.64
       Model |  12.3217334        19  .648512282   Prob > F        =    0.0000
    Residual |  110.528884       790   .13990998   R-squared       =    0.1003
-------------+----------------------------------   Adj R-squared   =    0.0787
       Total |  122.850617       809  .151854904   Root MSE        =    .37405

------------------------------------------------------------------------------
   mmUser_t1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   mmUser_t0 |   .0492856   .0635612     0.78   0.438    -.0754832    .1740544
             |
  districtID |
          2  |  -.1624073   .0655281    -2.48   0.013     -.291037   -.0337775
          3  |  -.1795181   .0828823    -2.17   0.031    -.3422136   -.0168226
          4  |  -.1822129   .0518552    -3.51   0.000    -.2840033   -.0804226
          5  |   .0043485   .0775937     0.06   0.955    -.1479657    .1566627
          6  |  -.0709967   .0661694    -1.07   0.284    -.2008853     .058892
          7  |  -.0339643   .0829995    -0.41   0.682    -.1968899    .1289612
          8  |   -.032962   .0463542    -0.71   0.477    -.1239541      .05803
          9  |   .0979962   .0400562     2.45   0.015     .0193671    .1766252
             |
     cfemale |  -.0553987   .0282033    -1.96   0.050    -.1107611   -.0000364
        cage |   -.000517   .0009407    -0.55   0.583    -.0023636    .0013296
    cmarried |  -.0385332   .0287057    -1.34   0.180    -.0948818    .0178153
       cakan |   .0609617   .0302753     2.01   0.044     .0015321    .1203913
cselfemplo~d |   .0555189   .0305132     1.82   0.069    -.0043777    .1154155
    cEducAny |   .0819021   .0455547     1.80   0.073    -.0075205    .1713246
 cselfIncome |   .0376399   .0189918     1.98   0.048     .0003595    .0749202
        trt2 |   .0486146   .0404102     1.20   0.229    -.0307095    .1279387
        trt3 |    .084436    .041329     2.04   0.041     .0033084    .1655636
        trt4 |   .0879759   .0402814     2.18   0.029     .0089046    .1670472
       _cons |   .5972811   .1007386     5.93   0.000     .3995342     .795028
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000
Warning: 100% of the resampled realizations for _pm_1 are exactly identical to
>  original value
Warning: 100% of the resampled realizations for _pm_2 are exactly identical to
>  original value

      Command: regress mmUser_t1 mmUser_t0 i.districtID cfemale cage
                   cmarried cakan cselfemployed cEducAny cselfIncome trt2
                   trt3 trt4
        _pm_1: trt3
        _pm_2: trt4
        _pm_3: _b[trt2]
        _pm_4: _b[trt3]
        _pm_5: _b[trt4]
  res. var(s):  trt2
   Resampling:  Permuting trt2
Clust. var(s):  loccode
     Clusters:  130
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_2 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_3 |   .0486146     155    1000  0.1550  0.0114  .1331109   .1789415
       _pm_4 |    .084436       0    1000  0.0000  0.0000         0   .0036821
       _pm_5 |   .0879759       0    1000  0.0000  0.0000         0   .0036821
------------------------------------------------------------------------------
Note: Confidence intervals are with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. ritest trt2 trt3 trt4 _b[trt2] _b[trt3] _b[trt4], reps($bootstrap_reps) clus
> ter(loccode) strata(districtID) seed(546): reg save_t1 save_t0 i.districtID 
> cfemale cage cmarried cakan cselfemployed cEducAny cselfIncome trt2 trt3 trt
> 4
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       810
-------------+----------------------------------   F(19, 790)      =      4.34
       Model |  16.1487913        19  .849936385   Prob > F        =    0.0000
    Residual |   154.74627       790  .195881355   R-squared       =    0.0945
-------------+----------------------------------   Adj R-squared   =    0.0727
       Total |  170.895062       809  .211242351   Root MSE        =    .44258

------------------------------------------------------------------------------
     save_t1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     save_t0 |   .1009415   .0355347     2.84   0.005     .0311879    .1706951
             |
  districtID |
          2  |  -.0689956   .0776857    -0.89   0.375    -.2214905    .0834993
          3  |  -.0477579   .0982378    -0.49   0.627    -.2405959    .1450801
          4  |  -.1990497   .0618773    -3.22   0.001    -.3205131   -.0775863
          5  |   .0044202    .093671     0.05   0.962    -.1794534    .1882938
          6  |  -.0415868   .0784723    -0.53   0.596    -.1956257     .112452
          7  |  -.0454431   .0989584    -0.46   0.646    -.2396956    .1488094
          8  |   .0522018   .0558237     0.94   0.350    -.0573785    .1617821
          9  |   .0093773   .0477966     0.20   0.845     -.084446    .1032005
             |
     cfemale |  -.1100998   .0334697    -3.29   0.001       -.1758   -.0443997
        cage |  -.0033185   .0011323    -2.93   0.003    -.0055413   -.0010958
    cmarried |  -.0204498   .0339692    -0.60   0.547    -.0871303    .0462307
       cakan |   .1042564   .0357907     2.91   0.004     .0340002    .1745125
cselfemplo~d |   .0306848   .0361585     0.85   0.396    -.0402933     .101663
    cEducAny |   .0209563   .0538967     0.39   0.698    -.0848413    .1267539
 cselfIncome |   .0218142   .0226054     0.96   0.335    -.0225596     .066188
        trt2 |   .0473035   .0478826     0.99   0.324    -.0466886    .1412957
        trt3 |   .0424749   .0489008     0.87   0.385    -.0535159    .1384658
        trt4 |   .1313802   .0476631     2.76   0.006     .0378189    .2249416
       _cons |   .6781341   .0976727     6.94   0.000     .4864052    .8698629
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000
Warning: 100% of the resampled realizations for _pm_1 are exactly identical to
>  original value
Warning: 100% of the resampled realizations for _pm_2 are exactly identical to
>  original value

      Command: regress save_t1 save_t0 i.districtID cfemale cage cmarried
                   cakan cselfemployed cEducAny cselfIncome trt2 trt3 trt4
        _pm_1: trt3
        _pm_2: trt4
        _pm_3: _b[trt2]
        _pm_4: _b[trt3]
        _pm_5: _b[trt4]
  res. var(s):  trt2
   Resampling:  Permuting trt2
Clust. var(s):  loccode
     Clusters:  130
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_2 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_3 |   .0473035     281    1000  0.2810  0.0142  .2533197    .309976
       _pm_4 |   .0424749       3    1000  0.0030  0.0017  .0006191    .008742
       _pm_5 |   .1313802       0    1000  0.0000  0.0000         0   .0036821
------------------------------------------------------------------------------
Note: Confidence intervals are with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. ritest trt2 trt3 trt4 _b[trt2] _b[trt3] _b[trt4], reps($bootstrap_reps) clus
> ter(loccode) strata(districtID) seed(546): reg score_MMoneyDd_t1 score_MMone
> yDd_t0 i.districtID cfemale cage cmarried cakan cselfemployed cEducAny cself
> Income trt2 trt3 trt4
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       810
-------------+----------------------------------   F(19, 790)      =      4.91
       Model |  77.0805893        19  4.05687312   Prob > F        =    0.0000
    Residual |  652.263015       790  .825649386   R-squared       =    0.1057
-------------+----------------------------------   Adj R-squared   =    0.0842
       Total |  729.343604       809  .901537211   Root MSE        =    .90865

------------------------------------------------------------------------------
score_MMon~1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
score_MMon~0 |   .1071308   .0722527     1.48   0.139    -.0346993    .2489608
             |
  districtID |
          2  |  -.3327509   .1588259    -2.10   0.036    -.6445217   -.0209802
          3  |  -.3394604   .2018381    -1.68   0.093    -.7356629     .056742
          4  |  -.3715539   .1259007    -2.95   0.003    -.6186932   -.1244145
          5  |   .0887691   .1890304     0.47   0.639    -.2822923    .4598304
          6  |  -.2036268   .1609991    -1.26   0.206    -.5196633    .1124098
          7  |  -.0763241   .2011077    -0.38   0.704    -.4710927    .3184446
          8  |  -.1112863   .1127399    -0.99   0.324    -.3325916    .1100189
          9  |   .2612342   .0977617     2.67   0.008     .0693307    .4531376
             |
     cfemale |  -.1682134   .0685374    -2.45   0.014    -.3027504   -.0336764
        cage |  -.0007615   .0022915    -0.33   0.740    -.0052597    .0037366
    cmarried |  -.0683318   .0697906    -0.98   0.328    -.2053287    .0686651
       cakan |   .1318688   .0735388     1.79   0.073    -.0124857    .2762232
cselfemplo~d |   .1468809   .0741328     1.98   0.048     .0013602    .2924015
    cEducAny |    .181907   .1108929     1.64   0.101    -.0357726    .3995865
 cselfIncome |   .1118506   .0463004     2.42   0.016     .0209642     .202737
        trt2 |   .1188358   .0981061     1.21   0.226    -.0737437    .3114153
        trt3 |   .2235645   .1004133     2.23   0.026      .026456    .4206729
        trt4 |    .226321    .097858     2.31   0.021     .0342284    .4184135
       _cons |   -.460059   .1970747    -2.33   0.020    -.8469111    -.073207
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000
Warning: 100% of the resampled realizations for _pm_1 are exactly identical to
>  original value
Warning: 100% of the resampled realizations for _pm_2 are exactly identical to
>  original value

      Command: regress score_MMoneyDd_t1 score_MMoneyDd_t0 i.districtID
                   cfemale cage cmarried cakan cselfemployed cEducAny
                   cselfIncome trt2 trt3 trt4
        _pm_1: trt3
        _pm_2: trt4
        _pm_3: _b[trt2]
        _pm_4: _b[trt3]
        _pm_5: _b[trt4]
  res. var(s):  trt2
   Resampling:  Permuting trt2
Clust. var(s):  loccode
     Clusters:  130
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_2 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_3 |   .1188358     148    1000  0.1480  0.0112  .1265527   .1715424
       _pm_4 |   .2235645       0    1000  0.0000  0.0000         0   .0036821
       _pm_5 |    .226321       0    1000  0.0000  0.0000         0   .0036821
------------------------------------------------------------------------------
Note: Confidence intervals are with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. **mht: implement Romano-Wolf (2005) procedure, pval
. rwolf ihs_mmtotamt_t1 mmUser_t1 save_t1 score_MMoneyDd_t1, indepvar(trt2 trt
> 3 trt4) reps($bootstrap_reps) seed(124) controls(i.districtID cfemale cage c
> married cakan cselfemployed cEducAny cselfIncome) //family (demand: amount, 
> 01 usage, 01 savings)
Bootstrap replications (1000). This may take some time.
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..................................................     50
..................................................     100
..................................................     150
..................................................     200
..................................................     250
..................................................     300
..................................................     350
..................................................     400
..................................................     450
..................................................     500
..................................................     550
..................................................     600
..................................................     650
..................................................     700
..................................................     750
..................................................     800
..................................................     850
..................................................     900
..................................................     950
..................................................     1000




Romano-Wolf step-down adjusted p-values


Independent variable:  trt2
Outcome variables:   ihs_mmtotamt_t1 mmUser_t1 save_t1 score_MMoneyDd_t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
    ihs_mmtotamt_t1 |     0.2794             0.3247              0.5145
          mmUser_t1 |     0.2420             0.2917              0.5145
            save_t1 |     0.4286             0.4555              0.5145
  score_MMoneyDd_t1 |     0.2409             0.2957              0.5135
------------------------------------------------------------------------------


Independent variable:  trt3
Outcome variables:   ihs_mmtotamt_t1 mmUser_t1 save_t1 score_MMoneyDd_t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
    ihs_mmtotamt_t1 |     0.0262             0.0290              0.0649
          mmUser_t1 |     0.0419             0.0410              0.0879
            save_t1 |     0.3894             0.4066              0.4066
  score_MMoneyDd_t1 |     0.0283             0.0320              0.0659
------------------------------------------------------------------------------


Independent variable:  trt4
Outcome variables:   ihs_mmtotamt_t1 mmUser_t1 save_t1 score_MMoneyDd_t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
    ihs_mmtotamt_t1 |     0.0272             0.0260              0.0430
          mmUser_t1 |     0.0299             0.0350              0.0430
            save_t1 |     0.0069             0.0150              0.0230
  score_MMoneyDd_t1 |     0.0199             0.0200              0.0340
------------------------------------------------------------------------------



. **attrition bounds
. **1. [Lee Bounds]**
. foreach x of varlist trt2 trt3 trt4 {
  2.         leebounds ihs_mmtotamt_t1 `x', level(95) cieffect tight() 
  3. }

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0447
Effect 95% conf. interval          : [-0.7164  0.4327]

------------------------------------------------------------------------------
ihs_mmtota~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt2         |
       lower |  -.3483574    .222654    -1.56   0.118    -.7847512    .0880364
       upper |   .0244914   .2469544     0.10   0.921    -.4595303    .5085131
------------------------------------------------------------------------------

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0209
Effect 95% conf. interval          : [-0.1597  0.8472]

------------------------------------------------------------------------------
ihs_mmtota~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt3         |
       lower |   .2746903   .2517197     1.09   0.275    -.2186712    .7680517
       upper |   .4490993   .2306974     1.95   0.052    -.0030592    .9012578
------------------------------------------------------------------------------

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0252
Effect 95% conf. interval          : [-0.1784  0.8059]

------------------------------------------------------------------------------
ihs_mmtota~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt4         |
       lower |    .182211   .2119947     0.86   0.390    -.2332909    .5977129
       upper |   .3883285   .2455235     1.58   0.114    -.0928887    .8695457
------------------------------------------------------------------------------

. *
. foreach x of varlist trt2 trt3 trt4 {
  2.         leebounds mmUser_t1 `x', level(95) cieffect tight() 
  3. }

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0447
Effect 95% conf. interval          : [-0.0894  0.0816]

------------------------------------------------------------------------------
   mmUser_t1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt2         |
       lower |  -.0345969   .0327817    -1.06   0.291    -.0988478     .029654
       upper |   .0121831   .0415251     0.29   0.769    -.0692046    .0935707
------------------------------------------------------------------------------

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0209
Effect 95% conf. interval          : [-0.0341  0.1139]

------------------------------------------------------------------------------
   mmUser_t1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt3         |
       lower |   .0386671   .0412495     0.94   0.349    -.0421805    .1195147
       upper |   .0600207   .0305104     1.97   0.049     .0002214    .1198199
------------------------------------------------------------------------------

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0252
Effect 95% conf. interval          : [-0.0146  0.1344]

------------------------------------------------------------------------------
   mmUser_t1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt4         |
       lower |   .0377061   .0300942     1.25   0.210    -.0212773    .0966896
       upper |   .0635682   .0407856     1.56   0.119    -.0163701    .1435065
------------------------------------------------------------------------------

. *
. foreach x of varlist trt2 trt3 trt4 {
  2.         leebounds save_t1 `x', level(95) cieffect tight() 
  3. }

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0447
Effect 95% conf. interval          : [-0.1200  0.0653]

------------------------------------------------------------------------------
     save_t1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt2         |
       lower |  -.0545132   .0391233    -1.39   0.164    -.1311934     .022167
       upper |  -.0077333   .0436262    -0.18   0.859    -.0932391    .0777726
------------------------------------------------------------------------------

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0209
Effect 95% conf. interval          : [-0.1040  0.0666]

------------------------------------------------------------------------------
     save_t1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt3         |
       lower |  -.0239569   .0450632    -0.53   0.595    -.1122791    .0643653
       upper |  -.0026033   .0389386    -0.07   0.947    -.0789216     .073715
------------------------------------------------------------------------------

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0252
Effect 95% conf. interval          : [ 0.0459  0.2078]

------------------------------------------------------------------------------
     save_t1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt4         |
       lower |    .107009   .0350474     3.05   0.002     .0383173    .1757007
       upper |   .1328711   .0429213     3.10   0.002     .0487468    .2169953
------------------------------------------------------------------------------

. *
. foreach x of varlist trt2 trt3 trt4 {
  2.         leebounds score_MMoneyDd_t1 `x', level(95) cieffect tight() 
  3. }

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0447
Effect 95% conf. interval          : [-0.2639  0.1086]

------------------------------------------------------------------------------
score_MMon~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt2         |
       lower |  -.1157872   .0831006    -1.39   0.164    -.2786614    .0470871
       upper |  -.0702584    .100319    -0.70   0.484    -.2668801    .1263633
------------------------------------------------------------------------------

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0209
Effect 95% conf. interval          : [-0.0456  0.3132]

------------------------------------------------------------------------------
score_MMon~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt3         |
       lower |   .1408469   .0999917     1.41   0.159    -.0551332     .336827
       upper |   .1621599   .0810062     2.00   0.045     .0033906    .3209292
------------------------------------------------------------------------------

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0252
Effect 95% conf. interval          : [-0.0516  0.2979]

------------------------------------------------------------------------------
score_MMon~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt4         |
       lower |   .0910302   .0765472     1.19   0.234    -.0589994    .2410599
       upper |    .112682   .0994454     1.13   0.257    -.0822274    .3075915
------------------------------------------------------------------------------

. *
. **2. [Behajel et al. Bounds]**
. foreach x of varlist ihs_mmtotamt_t1 mmUser_t1 save_t1 score_MMoneyDd_t1 {
  2.         preserve
  3.                 display "`x'"
  4.                 gen itemA= `x' if trt2==1 & attempts<=3 
  5.                 egen iranklo_Aa =rank(itemA) if trt2==1, unique //from ab
> ove
  6.                 egen iranklo_Ab =rank(-itemA) if trt2==1, unique //from b
> elow
  7.                 gen yupperA= `x'
  8.                 replace yupperA=. if (trt2==1 & iranklo_Aa<=20) | (trt2==
> 1 & attempts>3) //trim differences within 3 attempts and cut off all above 3
> -attempts
  9.                 gen ylowerA= `x'
 10.                 replace ylowerA=. if (trt2==1 & iranklo_Ab<=20) | (trt2==
> 1 & attempts>3)
 11.                 reg ylowerA  trt2, r
 12.                 reg yupperA trt2, r
 13.         restore
 14. }
ihs_mmtotamt_t1
(778 missing values generated)
(778 missing values generated)
(778 missing values generated)
(180 missing values generated)
(38 real changes made, 38 to missing)
(180 missing values generated)
(38 real changes made, 38 to missing)

Linear regression                               Number of obs     =        772
                                                F(1, 770)         =       7.10
                                                Prob > F          =     0.0079
                                                R-squared         =     0.0090
                                                Root MSE          =     2.4264

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt2 |  -.5342701   .2004621    -2.67   0.008    -.9277872   -.1407531
       _cons |   4.641552   .1011355    45.89   0.000     4.443018    4.840086
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        772
                                                F(1, 770)         =       1.84
                                                Prob > F          =     0.1752
                                                R-squared         =     0.0021
                                                Root MSE          =     2.3681

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt2 |   .2513972   .1852822     1.36   0.175     -.112321    .6151154
       _cons |   4.641552   .1011355    45.89   0.000     4.443018    4.840086
------------------------------------------------------------------------------
mmUser_t1
(778 missing values generated)
(778 missing values generated)
(778 missing values generated)
(180 missing values generated)
(38 real changes made, 38 to missing)
(180 missing values generated)
(38 real changes made, 38 to missing)

Linear regression                               Number of obs     =        772
                                                F(1, 770)         =       2.54
                                                Prob > F          =     0.1111
                                                R-squared         =     0.0036
                                                Root MSE          =     .39445

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt2 |  -.0550647   .0345168    -1.60   0.111    -.1228228    .0126935
       _cons |   .8206897   .0159493    51.46   0.000     .7893804    .8519989
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        772
                                                F(1, 770)         =       2.85
                                                Prob > F          =     0.0917
                                                R-squared         =     0.0032
                                                Root MSE          =     .37294

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt2 |    .049102   .0290823     1.69   0.092    -.0079879    .1061919
       _cons |   .8206897   .0159493    51.46   0.000     .7893804    .8519989
------------------------------------------------------------------------------
save_t1
(778 missing values generated)
(778 missing values generated)
(778 missing values generated)
(180 missing values generated)
(38 real changes made, 38 to missing)
(180 missing values generated)
(38 real changes made, 38 to missing)

Linear regression                               Number of obs     =        772
                                                F(1, 770)         =       2.97
                                                Prob > F          =     0.0855
                                                R-squared         =     0.0041
                                                Root MSE          =     .46145

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt2 |  -.0679957   .0394857    -1.72   0.085    -.1455081    .0095167
       _cons |   .7086207   .0188923    37.51   0.000     .6715341    .7457073
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        772
                                                F(1, 770)         =       0.97
                                                Prob > F          =     0.3251
                                                R-squared         =     0.0012
                                                Root MSE          =     .45047

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt2 |    .036171   .0367352     0.98   0.325    -.0359421     .108284
       _cons |   .7086207   .0188923    37.51   0.000     .6715341    .7457073
------------------------------------------------------------------------------
score_MMoneyDd_t1
(778 missing values generated)
(778 missing values generated)
(778 missing values generated)
(180 missing values generated)
(38 real changes made, 38 to missing)
(180 missing values generated)
(38 real changes made, 38 to missing)

Linear regression                               Number of obs     =        772
                                                F(1, 770)         =       4.78
                                                Prob > F          =     0.0291
                                                R-squared         =     0.0065
                                                Root MSE          =     .95304

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt2 |  -.1784849   .0816259    -2.19   0.029    -.3387206   -.0182493
       _cons |   .0199499   .0389992     0.51   0.609    -.0566074    .0965073
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        772
                                                F(1, 770)         =       2.55
                                                Prob > F          =     0.1110
                                                R-squared         =     0.0029
                                                Root MSE          =     .91033

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt2 |   .1127689   .0706832     1.60   0.111    -.0259857    .2515234
       _cons |   .0199499   .0389992     0.51   0.609    -.0566074    .0965073
------------------------------------------------------------------------------

. *
. 
. foreach x of varlist ihs_mmtotamt_t1 mmUser_t1 save_t1 score_MMoneyDd_t1 {
  2.         preserve
  3.                 display "`x'"
  4.                 gen itemA= `x' if trt3==1 & attempts<=3 
  5.                 egen iranklo_Aa =rank(itemA) if trt3==1, unique //from ab
> ove
  6.                 egen iranklo_Ab =rank(-itemA) if trt3==1, unique //from b
> elow
  7.                 gen yupperA= `x'
  8.                 replace yupperA=. if (trt3==1 & iranklo_Aa<=20) | (trt3==
> 1 & attempts>3) //trim differences within 3 attempts and cut off all above 3
> -attempts
  9.                 gen ylowerA= `x'
 10.                 replace ylowerA=. if (trt3==1 & iranklo_Ab<=20) | (trt3==
> 1 & attempts>3)
 11.                 reg ylowerA  trt3, r
 12.                 reg yupperA trt3, r
 13.         restore
 14. }
ihs_mmtotamt_t1
(791 missing values generated)
(791 missing values generated)
(791 missing values generated)
(180 missing values generated)
(28 real changes made, 28 to missing)
(180 missing values generated)
(28 real changes made, 28 to missing)

Linear regression                               Number of obs     =        782
                                                F(1, 780)         =       0.07
                                                Prob > F          =     0.7888
                                                R-squared         =     0.0001
                                                Root MSE          =     2.4111

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt3 |   .0516692   .1928481     0.27   0.789    -.3268936    .4302321
       _cons |   4.494571   .1005809    44.69   0.000     4.297129    4.692012
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        782
                                                F(1, 780)         =      32.44
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0283
                                                Root MSE          =     2.3269

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt3 |   .9437653   .1656966     5.70   0.000     .6185012    1.269029
       _cons |   4.494571   .1005809    44.69   0.000     4.297129    4.692012
------------------------------------------------------------------------------
mmUser_t1
(791 missing values generated)
(791 missing values generated)
(791 missing values generated)
(180 missing values generated)
(28 real changes made, 28 to missing)
(180 missing values generated)
(28 real changes made, 28 to missing)

Linear regression                               Number of obs     =        782
                                                F(1, 780)         =       1.95
                                                Prob > F          =     0.1634
                                                R-squared         =     0.0022
                                                Root MSE          =     .39279

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt3 |   .0442388   .0317136     1.39   0.163    -.0180154     .106493
       _cons |   .7993367   .0163304    48.95   0.000     .7672799    .8313934
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        782
                                                F(1, 780)         =      48.09
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0312
                                                Root MSE          =     .36578

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt3 |   .1559706   .0224903     6.94   0.000     .1118219    .2001193
       _cons |   .7993367   .0163304    48.95   0.000     .7672799    .8313934
------------------------------------------------------------------------------
save_t1
(791 missing values generated)
(791 missing values generated)
(791 missing values generated)
(180 missing values generated)
(28 real changes made, 28 to missing)
(180 missing values generated)
(28 real changes made, 28 to missing)

Linear regression                               Number of obs     =        782
                                                F(1, 780)         =       1.32
                                                Prob > F          =     0.2510
                                                R-squared         =     0.0018
                                                Root MSE          =     .46298

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt3 |  -.0462029   .0402156    -1.15   0.251    -.1251465    .0327408
       _cons |   .6998342   .0186886    37.45   0.000     .6631484      .73652
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        782
                                                F(1, 780)         =       3.17
                                                Prob > F          =     0.0754
                                                R-squared         =     0.0037
                                                Root MSE          =     .45123

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt3 |    .065529   .0368115     1.78   0.075    -.0067324    .1377903
       _cons |   .6998342   .0186886    37.45   0.000     .6631484      .73652
------------------------------------------------------------------------------
score_MMoneyDd_t1
(791 missing values generated)
(791 missing values generated)
(791 missing values generated)
(180 missing values generated)
(28 real changes made, 28 to missing)
(180 missing values generated)
(28 real changes made, 28 to missing)

Linear regression                               Number of obs     =        782
                                                F(1, 780)         =       0.66
                                                Prob > F          =     0.4181
                                                R-squared         =     0.0007
                                                Root MSE          =     .94904

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt3 |    .061714   .0761761     0.81   0.418    -.0878205    .2112485
       _cons |  -.0359942   .0395409    -0.91   0.363    -.1136134     .041625
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        782
                                                F(1, 780)         =      44.99
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0316
                                                Root MSE          =     .89248

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt3 |     .38299   .0571019     6.71   0.000     .2708983    .4950817
       _cons |  -.0359942   .0395409    -0.91   0.363    -.1136134     .041625
------------------------------------------------------------------------------

. *
. 
. foreach x of varlist ihs_mmtotamt_t1 mmUser_t1 save_t1 score_MMoneyDd_t1 {
  2.         preserve
  3.                 display "`x'"
  4.                 gen itemA= `x' if trt4==1 & attempts<=3 
  5.                 egen iranklo_Aa =rank(itemA) if trt4==1, unique //from ab
> ove
  6.                 egen iranklo_Ab =rank(-itemA) if trt4==1, unique //from b
> elow
  7.                 gen yupperA= `x'
  8.                 replace yupperA=. if (trt4==1 & iranklo_Aa<=20) | (trt4==
> 1 & attempts>3) //trim differences within 3 attempts and cut off all above 3
> -attempts
  9.                 gen ylowerA= `x'
 10.                 replace ylowerA=. if (trt4==1 & iranklo_Ab<=20) | (trt4==
> 1 & attempts>3)
 11.                 reg ylowerA  trt4, r
 12.                 reg yupperA trt4, r
 13.         restore
 14. }
ihs_mmtotamt_t1
(765 missing values generated)
(765 missing values generated)
(765 missing values generated)
(180 missing values generated)
(25 real changes made, 25 to missing)
(180 missing values generated)
(25 real changes made, 25 to missing)

Linear regression                               Number of obs     =        785
                                                F(1, 783)         =       0.00
                                                Prob > F          =     0.9809
                                                R-squared         =     0.0000
                                                Root MSE          =     2.4262

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt4 |  -.0044621   .1866948    -0.02   0.981    -.3709436    .3620195
       _cons |   4.514124   .1035455    43.60   0.000     4.310864    4.717384
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        785
                                                F(1, 783)         =      20.17
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0190
                                                Root MSE          =     2.3392

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt4 |   .7392955   .1646334     4.49   0.000     .4161204    1.062471
       _cons |   4.514124   .1035455    43.60   0.000     4.310864    4.717384
------------------------------------------------------------------------------
mmUser_t1
(765 missing values generated)
(765 missing values generated)
(765 missing values generated)
(180 missing values generated)
(25 real changes made, 25 to missing)
(180 missing values generated)
(25 real changes made, 25 to missing)

Linear regression                               Number of obs     =        785
                                                F(1, 783)         =       0.78
                                                Prob > F          =     0.3761
                                                R-squared         =     0.0009
                                                Root MSE          =     .39347

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt4 |   .0275442   .0310996     0.89   0.376    -.0335043    .0885926
       _cons |   .8017241   .0165763    48.37   0.000     .7691849    .8342634
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        785
                                                F(1, 783)         =      25.81
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0219
                                                Root MSE          =     .36811

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt4 |   .1251051   .0246258     5.08   0.000     .0767647    .1734456
       _cons |   .8017241   .0165763    48.37   0.000     .7691849    .8342634
------------------------------------------------------------------------------
save_t1
(765 missing values generated)
(765 missing values generated)
(765 missing values generated)
(180 missing values generated)
(25 real changes made, 25 to missing)
(180 missing values generated)
(25 real changes made, 25 to missing)

Linear regression                               Number of obs     =        785
                                                F(1, 783)         =       7.15
                                                Prob > F          =     0.0076
                                                R-squared         =     0.0082
                                                Root MSE          =     .46099

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt4 |   .0954584   .0356977     2.67   0.008     .0253839    .1655328
       _cons |   .6655172   .0196158    33.93   0.000     .6270114    .7040231
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        785
                                                F(1, 783)         =      38.07
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0353
                                                Root MSE          =      .4435

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt4 |   .1930193   .0312848     6.17   0.000     .1316073    .2544314
       _cons |   .6655172   .0196158    33.93   0.000     .6270114    .7040231
------------------------------------------------------------------------------
score_MMoneyDd_t1
(765 missing values generated)
(765 missing values generated)
(765 missing values generated)
(180 missing values generated)
(25 real changes made, 25 to missing)
(180 missing values generated)
(25 real changes made, 25 to missing)

Linear regression                               Number of obs     =        785
                                                F(1, 783)         =       0.31
                                                Prob > F          =     0.5779
                                                R-squared         =     0.0004
                                                Root MSE          =     .95185

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt4 |    .041317   .0742112     0.56   0.578    -.1043594    .1869935
       _cons |  -.0319961   .0403708    -0.79   0.428     -.111244    .0472517
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        785
                                                F(1, 783)         =      26.74
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0230
                                                Root MSE          =     .89861

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt4 |   .3131428    .060558     5.17   0.000     .1942675    .4320181
       _cons |  -.0319961   .0403708    -0.79   0.428     -.111244    .0472517
------------------------------------------------------------------------------

. *
. 
. 
. 
. **Quantifying: Bias belief vs Direct Price Effects
. *.......FROM ABOVE:
. *subjective beliefs
. gen iHave=(c4q17==1) if !missing(c4q17)
(65 missing values generated)

. gen iThink=(c8q3==1) if !missing(c8q3)
(2 missing values generated)

. gen i =(iHave==1 | iThink==1) if !missing(c4q17) | !missing(c8q3)
(2 missing values generated)

. sum iHave i _clocalpFraud  cfAttempts  iThink

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       iHave |        925    .2205405    .4148355          0          1
           i |        988    .4878543    .5001056          0          1
_clocalpFr~d |        988    .4878543    .5001056          0          1
  cfAttempts |        988    .5698381    .4953494          0          1
      iThink |        988    .4362348    .4961685          0          1

. 
. **1. base up-biased beliefs about misconduct
. replace text_ge01 = . if cdistrict_name == ""
(180 real changes made, 180 to missing)

. replace text_ge02 = . if clocality_name == ""
(180 real changes made, 180 to missing)

. // replace ge03 = . if vn == ""
. drop _merge

. 
. 
. *bring in audit objective misconduct data
. merge m:1 text_ge01 text_ge02 using "$dta_loc_repl/01_intermediate/ofdrate_m
> ktadminTransactData.dta"

    Result                      Number of obs
    -----------------------------------------
    Not matched                           203
        from master                       198  (_merge==1)
        from using                          5  (_merge==2)

    Matched                               792  (_merge==3)
    -----------------------------------------

. 
. *keep if _merge==3
. gen bias=(iThink != fdH0_t0) if !missing(iThink)
(7 missing values generated)

. bys text_ge01 text_ge02: egen bias_mkt = mean(bias) 
(5 missing values generated)

. sum bias_mkt, d

                          bias_mkt
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%     .0769231              0       Obs                 990
25%           .5              0       Sum of wgt.         990

50%        .9375                      Mean           .7232323
                        Largest       Std. dev.       .346795
75%            1              1
90%            1              1       Variance       .1202668
95%            1              1       Skewness      -.9431198
99%            1              1       Kurtosis       2.518455

. 
. *drop xB
. gen xB=(bias_mkt>0.9375) //bias: above median misrates at per market

. sum ihs_mmtotamt_t1 mmUser_t1 if trtment==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
ihs_mmtota~1 |        143    4.096911    2.660754          0   8.294049
   mmUser_t1 |        143    .7342657     .443276          0          1

. 
. ** Table C.17 --------------------------------------------------------------
> ----
. *pooled?
. 
. reg ihs_mmtotamt_t1 i.districtID ihs_mmtotamt_t0 cfemale cage cmarried cakan
>  cselfemployed cEducAny cselfIncome c.trtment if xB==1, cluster(loccode) lev
> el(95)

Linear regression                               Number of obs     =        312
                                                F(16, 52)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.1440
                                                Root MSE          =     2.1019

                               (Std. err. adjusted for 53 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
ihs_mmtota~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |  -1.496925   .2849901    -5.25   0.000    -2.068799   -.9250502
          3  |   2.334651   .5473056     4.27   0.000     1.236402    3.432901
          4  |  -.8384811   .4224911    -1.98   0.052    -1.686272    .0093092
          5  |   .9191344   .6817411     1.35   0.183    -.4488792    2.287148
          6  |  -.3790999   .7047029    -0.54   0.593     -1.79319     1.03499
          7  |   1.813599   .3467016     5.23   0.000     1.117891    2.509307
          8  |   .3029685   .5816209     0.52   0.605    -.8641392    1.470076
          9  |   .7560646   .3628647     2.08   0.042     .0279233    1.484206
             |
ihs_mmtota~0 |   .1233149   .0534325     2.31   0.025     .0160948     .230535
     cfemale |  -.1664279   .3175409    -0.52   0.602    -.8036204    .4707645
        cage |   .0042356   .0071686     0.59   0.557    -.0101493    .0186204
    cmarried |  -.0356374   .3254406    -0.11   0.913    -.6886817    .6174068
       cakan |  -.1889518   .3187172    -0.59   0.556    -.8285046    .4506009
cselfemplo~d |   .5904581   .3407168     1.73   0.089    -.0932402    1.274156
    cEducAny |    .100151   .3225265     0.31   0.757    -.5470458    .7473478
 cselfIncome |   .2855209    .125593     2.27   0.027     .0335001    .5375418
     trtment |   .9008344   .3175184     2.84   0.006     .2636873    1.537982
       _cons |   2.514934   .6895624     3.65   0.001     1.131226    3.898643
------------------------------------------------------------------------------

. reg ihs_mmtotamt_t1 i.districtID ihs_mmtotamt_t0 cfemale cage cmarried cakan
>  cselfemployed cEducAny cselfIncome c.trtment if xB==0, cluster(loccode) lev
> el(95)

Linear regression                               Number of obs     =        498
                                                F(17, 71)         =       4.72
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1059
                                                Root MSE          =     2.4684

                               (Std. err. adjusted for 72 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
ihs_mmtota~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |  -.5536621   .4041745    -1.37   0.175    -1.359563    .2522389
          3  |  -1.199043   .4094109    -2.93   0.005    -2.015385    -.382701
          4  |  -.7148157   .4338781    -1.65   0.104    -1.579944    .1503127
          5  |    .075244   .4775279     0.16   0.875    -.8769195    1.027408
          6  |  -.5871125   .4820742    -1.22   0.227    -1.548341    .3741161
          7  |  -.3704143    .595326    -0.62   0.536    -1.557461     .816632
          8  |  -.6146394   .3827433    -1.61   0.113    -1.377808    .1485291
          9  |    .610131   .2982143     2.05   0.044     .0155085    1.204753
             |
ihs_mmtota~0 |   .0422146    .055904     0.76   0.453    -.0692548     .153684
     cfemale |  -.5828506   .2866776    -2.03   0.046    -1.154469   -.0112317
        cage |  -.0029181   .0079255    -0.37   0.714    -.0187211    .0128849
    cmarried |   -.105777   .2459692    -0.43   0.668    -.5962257    .3846717
       cakan |    .547925   .3529129     1.55   0.125    -.1557633    1.251613
cselfemplo~d |   .3002665   .2895249     1.04   0.303    -.2770297    .8775627
    cEducAny |   .4418363   .4048151     1.09   0.279    -.3653422    1.249015
 cselfIncome |   .3863849   .1746233     2.21   0.030     .0381959    .7345739
     trtment |   .4012533   .2793941     1.44   0.155    -.1558428    .9583494
       _cons |   3.212416   .7684944     4.18   0.000     1.680082     4.74475
------------------------------------------------------------------------------

. 
. reg mmUser_t1 i.districtID mmUser_t0 cfemale cage cmarried cakan cselfemploy
> ed cEducAny cselfIncome c.trtment if xB==1, cluster(loccode) level(95)

Linear regression                               Number of obs     =        312
                                                F(16, 52)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.1062
                                                Root MSE          =     .32903

                               (Std. err. adjusted for 53 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
   mmUser_t1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |  -.1554207   .0443973    -3.50   0.001    -.2445104    -.066331
          3  |   .1983786   .0449015     4.42   0.000     .1082771    .2884801
          4  |  -.1741384   .0775825    -2.24   0.029    -.3298192   -.0184577
          5  |   .0643372   .0866474     0.74   0.461    -.1095336     .238208
          6  |  -.0428763   .1400662    -0.31   0.761    -.3239396    .2381871
          7  |   .2608769   .0699216     3.73   0.000     .1205689    .4011848
          8  |   .1125939   .0541331     2.08   0.042     .0039679    .2212199
          9  |   .0989835   .0377327     2.62   0.011     .0232672    .1746997
             |
   mmUser_t0 |   .0803658   .0889494     0.90   0.370    -.0981243    .2588558
     cfemale |  -.0425547   .0465844    -0.91   0.365     -.136033    .0509237
        cage |  -.0003666   .0010139    -0.36   0.719    -.0024012     .001668
    cmarried |   .0061987   .0466079     0.13   0.895    -.0873269    .0997243
       cakan |  -.0153444   .0429191    -0.36   0.722    -.1014678    .0707791
cselfemplo~d |   .0900975   .0527946     1.71   0.094    -.0158425    .1960376
    cEducAny |   .0187228   .0505603     0.37   0.713    -.0827339    .1201795
 cselfIncome |    .024166   .0152619     1.58   0.119    -.0064592    .0547911
     trtment |   .1237848   .0595789     2.08   0.043     .0042311    .2433386
       _cons |   .5850201   .1632316     3.58   0.001      .257472    .9125683
------------------------------------------------------------------------------

. reg mmUser_t1 i.districtID mmUser_t0 cfemale cage cmarried cakan cselfemploy
> ed cEducAny cselfIncome c.trtment if xB==0, cluster(loccode) level(95)

Linear regression                               Number of obs     =        498
                                                F(17, 71)         =       6.16
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1204
                                                Root MSE          =      .3963

                               (Std. err. adjusted for 72 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
   mmUser_t1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |  -.1754824   .0699144    -2.51   0.014    -.3148879    -.036077
          3  |  -.2739224   .0646042    -4.24   0.000    -.4027394   -.1451053
          4  |  -.2102269    .062094    -3.39   0.001    -.3340389    -.086415
          5  |  -.0674211   .0603936    -1.12   0.268    -.1878424    .0530003
          6  |  -.0882851   .0802852    -1.10   0.275    -.2483692    .0717991
          7  |  -.0697488   .0897012    -0.78   0.439    -.2486079    .1091102
          8  |  -.0879911   .0656238    -1.34   0.184    -.2188413    .0428591
          9  |   .0789155   .0410968     1.92   0.059    -.0030291    .1608602
             |
   mmUser_t0 |   .0451029   .1122305     0.40   0.689    -.1786783    .2688841
     cfemale |    -.05129   .0417983    -1.23   0.224    -.1346335    .0320535
        cage |  -.0007434    .001295    -0.57   0.568    -.0033256    .0018388
    cmarried |  -.0576754   .0378021    -1.53   0.132    -.1330506    .0176998
       cakan |   .1167347   .0530218     2.20   0.031     .0110122    .2224571
cselfemplo~d |   .0434784   .0508706     0.85   0.396    -.0579547    .1449116
    cEducAny |   .0920152   .0697086     1.32   0.191    -.0469797    .2310101
 cselfIncome |   .0517641   .0230262     2.25   0.028     .0058512     .097677
     trtment |    .069184   .0464054     1.49   0.140    -.0233457    .1617136
       _cons |    .582047   .1616612     3.60   0.001     .2597037    .9043902
------------------------------------------------------------------------------

. 
. *separate?
. reg ihs_mmtotamt_t1 i.districtID ihs_mmtotamt_t0 cfemale cage cmarried cakan
>  cselfemployed cEducAny cselfIncome i.trt if xB==1, cluster(loccode) level(9
> 5)

Linear regression                               Number of obs     =        312
                                                F(18, 52)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.1467
                                                Root MSE          =     2.1058

                               (Std. err. adjusted for 53 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
ihs_mmtota~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |  -1.475201   .2752731    -5.36   0.000    -2.027576   -.9228248
          3  |   2.219943   .5597608     3.97   0.000     1.096701    3.343185
          4  |  -.8604798   .4147954    -2.07   0.043    -1.692828   -.0281319
          5  |    .925093   .6893727     1.34   0.185    -.4582345    2.308421
          6  |   -.419017   .6777147    -0.62   0.539    -1.778951     .940917
          7  |   1.784751   .3559855     5.01   0.000     1.070414    2.499088
          8  |   .1748202   .6450423     0.27   0.787    -1.119552    1.469192
          9  |   .6451989   .3742091     1.72   0.091    -.1057067    1.396105
             |
ihs_mmtota~0 |   .1248678   .0562839     2.22   0.031     .0119259    .2378097
     cfemale |  -.1719977   .3056429    -0.56   0.576    -.7853151    .4413196
        cage |   .0042163   .0071739     0.59   0.559    -.0101791    .0186117
    cmarried |  -.0401323   .3218078    -0.12   0.901    -.6858869    .6056224
       cakan |  -.1453601   .3134202    -0.46   0.645    -.7742836    .4835635
cselfemplo~d |   .6145472   .3374671     1.82   0.074    -.0626301    1.291724
    cEducAny |   .1208891   .3354687     0.36   0.720     -.552278    .7940562
 cselfIncome |   .2910459   .1298053     2.24   0.029     .0305725    .5515193
             |
         trt |
          1  |   .8434718   .3690376     2.29   0.026     .1029437       1.584
          2  |   1.135473   .4385982     2.59   0.012      .255361    2.015585
          3  |   .8239646   .3765914     2.19   0.033     .0682786    1.579651
             |
       _cons |   2.493427   .7005454     3.56   0.001      1.08768    3.899174
------------------------------------------------------------------------------

. reg ihs_mmtotamt_t1 i.districtID ihs_mmtotamt_t0 cfemale cage cmarried cakan
>  cselfemployed cEducAny cselfIncome i.trt if xB==0, cluster(loccode) level(9
> 5)

Linear regression                               Number of obs     =        498
                                                F(19, 71)         =       5.96
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1103
                                                Root MSE          =     2.4675

                               (Std. err. adjusted for 72 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
ihs_mmtota~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |  -.5582358   .4583105    -1.22   0.227    -1.472081    .3556093
          3  |  -1.181799   .4730345    -2.50   0.015    -2.125003   -.2385945
          4  |  -.6681284   .4907252    -1.36   0.178    -1.646607    .3103498
          5  |  -4.28e-06   .5104012    -0.00   1.000    -1.017715    1.017707
          6  |  -.5358181   .5089066    -1.05   0.296    -1.550549    .4789128
          7  |  -.4054415   .5481114    -0.74   0.462    -1.498345    .6874616
          8  |  -.5793781   .4050369    -1.43   0.157    -1.386999    .2282424
          9  |   .5999395   .2811269     2.13   0.036     .0393884    1.160491
             |
ihs_mmtota~0 |   .0446288   .0569516     0.78   0.436    -.0689295    .1581872
     cfemale |  -.5684001   .2840804    -2.00   0.049     -1.13484   -.0019599
        cage |   -.002297   .0079171    -0.29   0.773    -.0180832    .0134891
    cmarried |  -.1325518   .2473049    -0.54   0.594    -.6256638    .3605603
       cakan |    .557829   .3601801     1.55   0.126    -.1603498    1.276008
cselfemplo~d |   .3091544   .2922133     1.06   0.294    -.2735023    .8918112
    cEducAny |   .4477452   .4088445     1.10   0.277    -.3674677    1.262958
 cselfIncome |   .3851792   .1766723     2.18   0.033     .0329046    .7374538
             |
         trt |
          1  |   .1907174   .3455319     0.55   0.583    -.4982536    .8796884
          2  |   .3594413   .3390332     1.06   0.293    -.3165718    1.035454
          3  |   .6605159   .3219622     2.05   0.044     .0185415     1.30249
             |
       _cons |    3.15361   .8053621     3.92   0.000     1.547764    4.759456
------------------------------------------------------------------------------

. 
. reg mmUser_t1 i.districtID mmUser_t0 cfemale cage cmarried cakan cselfemploy
> ed cEducAny cselfIncome i.trt if xB==1, cluster(loccode) level(95)

Linear regression                               Number of obs     =        312
                                                F(18, 52)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.1072
                                                Root MSE          =     .32997

                               (Std. err. adjusted for 53 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
   mmUser_t1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |  -.1650008   .0429621    -3.84   0.000    -.2512106    -.078791
          3  |   .1931942   .0494938     3.90   0.000     .0938777    .2925107
          4  |  -.1750969   .0758892    -2.31   0.025    -.3273797   -.0228141
          5  |   .0678274   .0874808     0.78   0.442    -.1077158    .2433705
          6  |  -.0535349   .1391352    -0.38   0.702      -.33273    .2256602
          7  |   .2553321   .0716404     3.56   0.001     .1115752     .399089
          8  |   .0992675   .0577455     1.72   0.092    -.0166073    .2151423
          9  |   .0921534   .0436028     2.11   0.039     .0046579    .1796488
             |
   mmUser_t0 |   .0787777   .0898047     0.88   0.384    -.1014285    .2589839
     cfemale |   -.044405   .0461517    -0.96   0.340    -.1370151    .0482052
        cage |  -.0003997   .0010292    -0.39   0.699     -.002465    .0016656
    cmarried |    .005789   .0467454     0.12   0.902    -.0880126    .0995905
       cakan |  -.0154232   .0425908    -0.36   0.719     -.100888    .0700415
cselfemplo~d |   .0915836   .0526214     1.74   0.088    -.0140091    .1971762
    cEducAny |   .0205303    .052038     0.39   0.695    -.0838916    .1249521
 cselfIncome |   .0244445   .0150335     1.63   0.110    -.0057225    .0546114
             |
         trt |
          1  |   .1087265   .0645835     1.68   0.098    -.0208697    .2383228
          2  |   .1385245   .0697966     1.98   0.052    -.0015326    .2785816
          3  |   .1297268   .0655536     1.98   0.053    -.0018161    .2612697
             |
       _cons |   .5907305   .1646906     3.59   0.001     .2602546    .9212063
------------------------------------------------------------------------------

. reg mmUser_t1 i.districtID mmUser_t0 cfemale cage cmarried cakan cselfemploy
> ed cEducAny cselfIncome i.trt if xB==0, cluster(loccode) level(95)

Linear regression                               Number of obs     =        498
                                                F(19, 71)         =       5.98
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1226
                                                Root MSE          =     .39662

                               (Std. err. adjusted for 72 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
   mmUser_t1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |  -.1793386   .0751748    -2.39   0.020    -.3292328   -.0294443
          3  |  -.2759308   .0734544    -3.76   0.000    -.4223947   -.1294669
          4  |  -.2096727   .0677237    -3.10   0.003    -.3447099   -.0746355
          5  |  -.0772821   .0633035    -1.22   0.226    -.2035057    .0489415
          6  |   -.080467   .0864477    -0.93   0.355    -.2528389    .0919048
          7  |  -.0756447   .0848347    -0.89   0.376    -.2448002    .0935108
          8  |  -.0893693   .0669319    -1.34   0.186    -.2228278    .0440892
          9  |   .0750253   .0401879     1.87   0.066    -.0051071    .1551578
             |
   mmUser_t0 |   .0429026   .1117448     0.38   0.702    -.1799102    .2657154
     cfemale |  -.0501386   .0417337    -1.20   0.234    -.1333534    .0330761
        cage |  -.0006837   .0012982    -0.53   0.600    -.0032722    .0019049
    cmarried |  -.0610858   .0383194    -1.59   0.115    -.1374926     .015321
       cakan |   .1197196   .0534776     2.24   0.028     .0130882     .226351
cselfemplo~d |   .0460429   .0518039     0.89   0.377    -.0572512     .149337
    cEducAny |   .0918775   .0705482     1.30   0.197    -.0487917    .2325466
 cselfIncome |    .051387   .0225968     2.27   0.026     .0063303    .0964437
             |
         trt |
          1  |   .0533093   .0590058     0.90   0.369    -.0643449    .1709635
          2  |   .0538709   .0523435     1.03   0.307     -.050499    .1582409
          3  |   .1015136   .0536224     1.89   0.062    -.0054064    .2084337
             |
       _cons |    .580823   .1624248     3.58   0.001     .2569572    .9046888
------------------------------------------------------------------------------

. reg mmUser_t1 i.districtID mmUser_t0 cfemale cage cmarried cakan cselfemploy
> ed cEducAny cselfIncome i.trt if xB==0, r level(95)

Linear regression                               Number of obs     =        498
                                                F(19, 478)        =       3.72
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1226
                                                Root MSE          =     .39662

------------------------------------------------------------------------------
             |               Robust
   mmUser_t1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |  -.1793386   .0890142    -2.01   0.044     -.354246   -.0044311
          3  |  -.2759308   .1226443    -2.25   0.025    -.5169193   -.0349423
          4  |  -.2096727   .0772086    -2.72   0.007    -.3613829   -.0579625
          5  |  -.0772821   .0919986    -0.84   0.401    -.2580538    .1034896
          6  |   -.080467    .116162    -0.69   0.489    -.3087184    .1477843
          7  |  -.0756447   .1027992    -0.74   0.462    -.2776388    .1263494
          8  |  -.0893693   .0647879    -1.38   0.168    -.2166736     .037935
          9  |   .0750253    .051212     1.46   0.144    -.0256031    .1756538
             |
   mmUser_t0 |   .0429026   .0953337     0.45   0.653    -.1444223    .2302275
     cfemale |  -.0501386   .0383345    -1.31   0.192    -.1254636    .0251864
        cage |  -.0006837   .0012542    -0.55   0.586    -.0031481    .0017807
    cmarried |  -.0610858    .038232    -1.60   0.111    -.1362093    .0140377
       cakan |   .1197196    .042946     2.79   0.006     .0353334    .2041058
cselfemplo~d |   .0460429   .0424728     1.08   0.279    -.0374136    .1294994
    cEducAny |   .0918775   .0703724     1.31   0.192    -.0464001     .230155
 cselfIncome |    .051387   .0263466     1.95   0.052    -.0003825    .1031565
             |
         trt |
          1  |   .0533093   .0556254     0.96   0.338    -.0559913    .1626099
          2  |   .0538709    .053205     1.01   0.312    -.0506737    .1584155
          3  |   .1015136   .0511415     1.98   0.048     .0010237    .2020035
             |
       _cons |    .580823   .1451017     4.00   0.000     .2957071     .865939
------------------------------------------------------------------------------

. 
. 
. **if assume identical price sensitibity - (balance test, yes-some balance)
. reg cfemale xB, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(1, 129)         =       0.82
                                                Prob > F          =     0.3677
                                                R-squared         =     0.0011
                                                Root MSE          =     .48386

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
     cfemale | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          xB |  -.0315723   .0349241    -0.90   0.368    -.1006705    .0375259
       _cons |   .6425703   .0274327    23.42   0.000      .588294    .6968466
------------------------------------------------------------------------------

. reg cmarried xB, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(1, 129)         =       1.60
                                                Prob > F          =     0.2088
                                                R-squared         =     0.0019
                                                Root MSE          =     .49852

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
    cmarried | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          xB |  -.0429294   .0339871    -1.26   0.209    -.1101736    .0243149
       _cons |    .560241    .027911    20.07   0.000     .5050183    .6154636
------------------------------------------------------------------------------

. reg cakan xB, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(1, 129)         =       0.00
                                                Prob > F          =     0.9888
                                                R-squared         =     0.0000
                                                Root MSE          =     .48516

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
       cakan | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          xB |    .000728    .051684     0.01   0.989    -.1015301     .102986
       _cons |     .62249   .0457132    13.62   0.000     .5320453    .7129346
------------------------------------------------------------------------------

. reg cage xB, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(1, 129)         =       2.90
                                                Prob > F          =     0.0910
                                                R-squared         =     0.0039
                                                Root MSE          =     15.195

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
        cage | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          xB |  -1.910567   1.122006    -1.70   0.091    -4.130483    .3093491
       _cons |   41.27309   .7775774    53.08   0.000     39.73464    42.81155
------------------------------------------------------------------------------

. reg cEducAny xB, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(1, 129)         =       0.99
                                                Prob > F          =     0.3208
                                                R-squared         =     0.0017
                                                Root MSE          =     .30018

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
    cEducAny | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          xB |   .0248734   .0249549     1.00   0.321    -.0245004    .0742472
       _cons |   .8875502   .0196211    45.23   0.000     .8487293    .9263711
------------------------------------------------------------------------------

. reg cselfemployed xB, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(1, 129)         =      11.99
                                                Prob > F          =     0.0007
                                                R-squared         =     0.0191
                                                Root MSE          =     .45786

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
cselfemplo~d | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          xB |  -.1277288   .0368836    -3.46   0.001     -.200704   -.0547537
       _cons |   .7550201   .0299804    25.18   0.000     .6957031    .8143371
------------------------------------------------------------------------------

. reg cselfIncome xB, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(1, 129)         =       0.37
                                                Prob > F          =     0.5416
                                                R-squared         =     0.0009
                                                Root MSE          =     .76188

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
 cselfIncome | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          xB |   .0446838   .0730137     0.61   0.542    -.0997755    .1891432
       _cons |   1.277108   .0541928    23.57   0.000     1.169887     1.38433
------------------------------------------------------------------------------

. reg cMMoneyregistered xB, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(1, 129)         =       4.89
                                                Prob > F          =     0.0288
                                                R-squared         =     0.0066
                                                Root MSE          =     .29399

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
cMMoneyreg~d | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          xB |  -.0478738   .0216464    -2.21   0.029    -.0907018   -.0050458
       _cons |   .9277108    .012738    72.83   0.000     .9025083    .9529134
------------------------------------------------------------------------------

. reg xB cfemale cmarried cakan cage cEducAny cselfemployed cselfIncome cMMone
> yregistered, cluster(loccode)

Linear regression                               Number of obs     =        989
                                                F(8, 129)         =       3.62
                                                Prob > F          =     0.0008
                                                R-squared         =     0.0313
                                                Root MSE          =     .49436

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
          xB | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     cfemale |  -.0290378   .0383926    -0.76   0.451    -.1049985    .0469229
    cmarried |  -.0028485    .036696    -0.08   0.938    -.0754524    .0697555
       cakan |   .0146272   .0520855     0.28   0.779    -.0884253    .1176797
        cage |   -.001452   .0012755    -1.14   0.257    -.0039755    .0010716
    cEducAny |   .0378666   .0677598     0.56   0.577    -.0961979    .1719311
cselfemplo~d |  -.1409957   .0447619    -3.15   0.002    -.2295581   -.0524332
 cselfIncome |   .0187513   .0321458     0.58   0.561      -.04485    .0823525
cMMoneyreg~d |  -.1603967   .0563247    -2.85   0.005    -.2718365    -.048957
       _cons |   .7497035   .1113453     6.73   0.000     .5294041     .970003
------------------------------------------------------------------------------

. test cfemale cmarried cakan cage cEducAny cselfemployed cselfIncome cMMoneyr
> egistered

 ( 1)  cfemale = 0
 ( 2)  cmarried = 0
 ( 3)  cakan = 0
 ( 4)  cage = 0
 ( 5)  cEducAny = 0
 ( 6)  cselfemployed = 0
 ( 7)  cselfIncome = 0
 ( 8)  cMMoneyregistered = 0

       F(  8,   129) =    3.62
            Prob > F =    0.0008

. probit xB cfemale cakan cmarried cage cEducAny cselfemployed cselfIncome cMM
> oneyregistered, cluster(loccode)

Iteration 0:  Log pseudolikelihood = -685.49779  
Iteration 1:  Log pseudolikelihood = -669.82345  
Iteration 2:  Log pseudolikelihood = -669.81505  
Iteration 3:  Log pseudolikelihood = -669.81505  

Probit regression                                       Number of obs =    989
                                                        Wald chi2(8)  =  27.27
                                                        Prob > chi2   = 0.0006
Log pseudolikelihood = -669.81505                       Pseudo R2     = 0.0229

                              (Std. err. adjusted for 130 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
          xB | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     cfemale |   -.074448   .0979877    -0.76   0.447    -.2665004    .1176045
       cakan |   .0370223   .1336126     0.28   0.782    -.2248536    .2988983
    cmarried |  -.0070244   .0938081    -0.07   0.940    -.1908849    .1768361
        cage |  -.0036705   .0032514    -1.13   0.259    -.0100432    .0027022
    cEducAny |   .0981084   .1744843     0.56   0.574    -.2438746    .4400913
cselfemplo~d |  -.3602608    .114946    -3.13   0.002    -.5855508   -.1349708
 cselfIncome |   .0491248   .0836905     0.59   0.557    -.1149056    .2131552
cMMoneyreg~d |  -.4137334   .1476841    -2.80   0.005    -.7031889   -.1242779
       _cons |   .6387078   .2883195     2.22   0.027      .073612    1.203804
------------------------------------------------------------------------------

. test cfemale cmarried cakan cage cEducAny cselfemployed cselfIncome cMMoneyr
> egistered

 ( 1)  [xB]cfemale = 0
 ( 2)  [xB]cmarried = 0
 ( 3)  [xB]cakan = 0
 ( 4)  [xB]cage = 0
 ( 5)  [xB]cEducAny = 0
 ( 6)  [xB]cselfemployed = 0
 ( 7)  [xB]cselfIncome = 0
 ( 8)  [xB]cMMoneyregistered = 0

           chi2(  8) =   27.27
         Prob > chi2 =    0.0006

. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
end of do-file

. do "$do_loc/Misconduct_Mar.19.2023.do" // 5-ish minutes?

. /*JPE2023-Annan
> **y = Misconduct*
> 
> Outline:
>         - Main results
>         - Spillovers
>         - Heterogeneity: Vendor Competition & Gender
> 
> Input:
>         - FINAL AUDIT DATA/_Francis/analyzed_EndlineAuditData.dta
>         - data-Mgt/Stats?/Mkt_census_xtics_+_interventions_localized.dta
>         - data-Mgt/Stats?/adminTransactData
>         - data-Mgt/Stats?/InterventionsLocalitiesList.dta
>         
>         - FINAL AUDIT DATA/_Francis/analyzed_EndlineAuditData.dta
>         - data-Mgt/Stats?/pct_female_MktcensusStar
>         - sampling?/Treatments_4gps_9dist
>         
>         - FINAL AUDIT DATA/_Francis/analyzed_EndlineAuditData.dta
>         - FINAL AUDIT DATA/_Francis/mkt_aiVendorBetter.dta
>         
> Output:
>         - data-Mgt/Stats?/InterventionsLocalitiesList.dta
> 
> */
. 
. 
. use "$dta_loc_repl/00_Raw_anon/analyzed_EndlineAuditData.dta", clear

. 
. ** Table 2 -----------------------------------------------------------------
> ----
. *Main Results: DIRECT EFFECTS*
. gen ihs_fdamt = asinh(fdamt) //NOTE: fdamt recoded as 0 if fd=0 (if no overc
> harging occurs), so, no material diff b/n (ii) fdamt and (iii) ihs_fdamt
(1,164 missing values generated)

. egen xbar = mean(trt)

. egen ybar = mean(fdamt)

. sum fd fdamt ihs_fdamt if trt==0 

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
          fd |        285    .2947368    .4567264          0          1
       fdamt |        285    .7789474     1.47881          0          5
   ihs_fdamt |        285    .4538683    .7768156          0   2.312438

. sum fd fdamt ihs_fdamt if trt==0 & _merge==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
          fd |         97    .2783505    .4505152          0          1
       fdamt |         97    .7835052    1.522243          0          5
   ihs_fdamt |         97    .4459032    .7909557          0   2.312438

. 
. gen uniqueVendorID = ge03

. 
. *y = trt + distXtrXdateFes + y_base + x_all6 + e*
. reg fd i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a i.m3
> q1 trt, r cluster(uniqueVendorID) level(95) //only rep vendors

Linear regression                               Number of obs     =        335
                                                F(74, 90)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6871
                                                Root MSE          =     .29115

                        (Std. err. adjusted for 91 clusters in uniqueVendorID)
------------------------------------------------------------------------------
             |               Robust
          fd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.1793444   .1167869    -1.54   0.128    -.4113621    .0526732
          5  |   .3993606   .3773529     1.06   0.293    -.3503169    1.149038
          6  |  -.0266847   .1548091    -0.17   0.864      -.33424    .2808706
          7  |  -.1793444   .1167869    -1.54   0.128    -.4113621    .0526732
          8  |   .0005637   .0693092     0.01   0.994    -.1371312    .1382586
         11  |   .0063325    .089923     0.07   0.944    -.1723153    .1849803
         14  |   .0063325    .089923     0.07   0.944    -.1723153    .1849803
         18  |  -.0266847   .1548091    -0.17   0.864      -.33424    .2808706
         22  |  -.1793444   .1167869    -1.54   0.128    -.4113621    .0526732
         23  |  -.0084815   .0669629    -0.13   0.899     -.141515    .1245521
         24  |  -.0266847   .1548091    -0.17   0.864      -.33424    .2808706
         26  |  -.0862906   .1308207    -0.66   0.511    -.3461887    .1736075
         27  |  -.0266847   .1548091    -0.17   0.864      -.33424    .2808706
         29  |  -.0955418    .132337    -0.72   0.472    -.3584525    .1673688
         30  |  -.0266847   .1548091    -0.17   0.864      -.33424    .2808706
         32  |   .1031431   .2009629     0.51   0.609    -.2961047     .502391
         33  |  -.0266847   .1548091    -0.17   0.864      -.33424    .2808706
         34  |  -.1793444   .1167869    -1.54   0.128    -.4113621    .0526732
         35  |  -.0221973   .0649497    -0.34   0.733    -.1512312    .1068366
         37  |  -.0773831   .0776867    -1.00   0.322    -.2317213     .076955
         38  |  -.0057002   .1537024    -0.04   0.970    -.3110568    .2996564
         39  |  -.0707246   .0749631    -0.94   0.348    -.2196519    .0782026
         40  |    .192584   .2193983     0.88   0.382     -.243289     .628457
         41  |   .0609962   .2043907     0.30   0.766    -.3450615     .467054
         42  |   .3021707   .2736264     1.10   0.272     -.241436    .8457774
         51  |  -.0930925   .0734891    -1.27   0.209    -.2390915    .0529065
         52  |  -.2194381    .115415    -1.90   0.060    -.4487302    .0098541
         53  |  -.0757409    .079724    -0.95   0.345    -.2341265    .0826448
         54  |  -.0419175   .1413527    -0.30   0.767    -.3227393    .2389043
         55  |  -.0694364   .0927823    -0.75   0.456    -.2537647    .1148919
         56  |  -.0122097    .118275    -0.10   0.918    -.2471837    .2227643
         57  |   .8351284   .0900564     9.27   0.000     .6562156    1.014041
         58  |   .8201964    .166735     4.92   0.000     .4889481    1.151445
         61  |  -.0767719   .0998652    -0.77   0.444    -.2751716    .1216278
         62  |  -.1314983   .0831401    -1.58   0.117    -.2966706    .0336741
         63  |   -.114972   .1236666    -0.93   0.355    -.3606572    .1307132
         64  |  -.2668609   .1127605    -2.37   0.020    -.4908794   -.0428425
         65  |   .0077154   .1390522     0.06   0.956     -.268536    .2839669
         66  |   .8347112    .121712     6.86   0.000     .5929091    1.076513
         67  |   .4671359   .2301224     2.03   0.045     .0099576    .9243142
         68  |  -.2668609   .1127605    -2.37   0.020    -.4908794   -.0428425
         69  |   .0558884   .1164373     0.48   0.632    -.1754347    .2872115
         71  |   .3207166   .2451542     1.31   0.194     -.166325    .8077582
         72  |  -.2668609   .1127605    -2.37   0.020    -.4908794   -.0428425
         89  |   .0260916   .1063471     0.25   0.807    -.1851854    .2373687
         90  |  -.1434131   .1129321    -1.27   0.207    -.3677724    .0809461
         91  |  -.1733038   .1656309    -1.05   0.298    -.5023585     .155751
         93  |   .0502677   .0925043     0.54   0.588    -.1335083    .2340438
         94  |  -.2098505   .1353133    -1.55   0.124     -.478674     .058973
         95  |  -.1832488   .1316594    -1.39   0.167    -.4448132    .0783155
         97  |  -.0312939   .0855038    -0.37   0.715     -.201162    .1385743
         98  |  -.1314983   .0831401    -1.58   0.117    -.2966706    .0336741
         99  |  -.1503164   .1499785    -1.00   0.319    -.4482748    .1476421
        102  |   .3911878   .5716011     0.68   0.495    -.7443975    1.526773
        103  |   1.062224   .0754935    14.07   0.000     .9122432    1.212205
        104  |   .6440157   .1350621     4.77   0.000     .3756913    .9123401
        107  |   .0125927    .081411     0.15   0.877    -.1491445    .1743298
        109  |  -.1577494   .1006372    -1.57   0.121    -.3576828    .0421839
        110  |  -.0780097   .0942533    -0.83   0.410    -.2652603    .1092409
        113  |  -.0985944   .0798242    -1.24   0.220    -.2571792    .0599903
        114  |  -.1711272   .1851885    -0.92   0.358    -.5390364    .1967821
        117  |  -.0985944   .0798242    -1.24   0.220    -.2571792    .0599903
        118  |  -.2132722   .0984401    -2.17   0.033    -.4088407   -.0177038
        137  |  -.1877178     .09124    -2.06   0.043    -.3689819   -.0064537
        138  |  -.1514245   .1019454    -1.49   0.141    -.3539568    .0511078
        141  |  -.2169045   .0991712    -2.19   0.031    -.4139254   -.0198835
        142  |  -.2172775    .107787    -2.02   0.047    -.4314153   -.0031398
        145  |  -.1277811   .0828624    -1.54   0.127    -.2924018    .0368396
        146  |  -.1679374   .1015517    -1.65   0.102    -.3696876    .0338128
        149  |   .3868122   .4806977     0.80   0.423    -.5681776    1.341802
        150  |   .8530504   .1377969     6.19   0.000     .5792927    1.126808
        153  |  -.0985944   .0798242    -1.24   0.220    -.2571792    .0599903
        154  |  -.0043941    .103672    -0.04   0.966    -.2103565    .2015684
        157  |   -.090423   .1154253    -0.78   0.435    -.3197355    .1388895
        158  |  -.0127429    .164546    -0.08   0.938    -.3396423    .3141565
        159  |  -.0178226   .0706444    -0.25   0.801      -.15817    .1225248
        160  |   .8739813   .1428915     6.12   0.000     .5901024     1.15786
        162  |   .8739813   .1428915     6.12   0.000     .5901024     1.15786
        171  |  -.1630235   .1444841    -1.13   0.262    -.4500664    .1240195
        172  |   .1005329    .120494     0.83   0.406    -.1388494    .3399152
        173  |  -.1630235   .1444841    -1.13   0.262    -.4500664    .1240195
        174  |  -.1260187   .1428915    -0.88   0.380    -.4098976    .1578601
        175  |   -.090423   .1154253    -0.78   0.435    -.3197355    .1388895
        176  |  -.1260187   .1428915    -0.88   0.380    -.4098976    .1578601
        177  |   .8930541   .0851776    10.48   0.000      .723834    1.062274
        178  |   .4872571   .3968732     1.23   0.223    -.3012009    1.275715
        180  |   .4872571   .3968732     1.23   0.223    -.3012009    1.275715
        181  |  -.2355559   .1274889    -1.85   0.068     -.488835    .0177231
        182  |  -.0537621   .0961948    -0.56   0.578    -.2448698    .1373456
        183  |   .8294295   .1103021     7.52   0.000     .6102951    1.048564
        184  |   .2311384   .3947246     0.59   0.560    -.5530509    1.015328
        185  |   -.229558   .1072169    -2.14   0.035     -.442563    -.016553
        186  |  -.0395988   .1140253    -0.35   0.729      -.26613    .1869324
        187  |  -.1705705   .1103021    -1.55   0.126    -.3897049    .0485638
        189  |  -.1705705   .1103021    -1.55   0.126    -.3897049    .0485638
        191  |  -.1705705   .1103021    -1.55   0.126    -.3897049    .0485638
        193  |  -.1705705   .1103021    -1.55   0.126    -.3897049    .0485638
        195  |  -.2060622   .1080225    -1.91   0.060    -.4206678    .0085434
        196  |   .4444138   .4234431     1.05   0.297    -.3968299    1.285657
        197  |    .149147   .4168376     0.36   0.721    -.6789737    .9772677
        198  |  -.1293719   .0883544    -1.46   0.147    -.3049032    .0461594
        199  |  -.1841864   .0912395    -2.02   0.046    -.3654496   -.0029231
        200  |  -.0726646   .0872057    -0.83   0.407    -.2459139    .1005848
        201  |   .7999357   .0924047     8.66   0.000     .6163578    .9835137
        202  |   .9597514   .0665723    14.42   0.000     .8274939    1.092009
        203  |   .8294295   .1103021     7.52   0.000     .6102951    1.048564
        204  |  -.0820888   .0947903    -0.87   0.389    -.2704063    .1062288
        205  |  -.0720387   .0897243    -0.80   0.424    -.2502916    .1062142
        206  |    .447909   .4246911     1.05   0.294    -.3958139    1.291632
        207  |    .170284   .2643886     0.64   0.521    -.3549701     .695538
        210  |  -.0801658   .0893165    -0.90   0.372    -.2576087    .0972771
        211  |  -.1395814   .0974778    -1.43   0.156     -.333238    .0540753
        212  |  -.1210365   .0887659    -1.36   0.176    -.2973855    .0553125
        213  |  -.0936177   .1159567    -0.81   0.422     -.323986    .1367506
        214  |  -.1387672   .1263792    -1.10   0.275    -.3898415     .112307
        215  |   .8789635   .0887659     9.90   0.000     .7026145    1.055313
        216  |   .4503561   .4282354     1.05   0.296    -.4004083    1.301121
        217  |   .2957459   .3527827     0.84   0.404    -.4051184    .9966102
        219  |   .3530052   .3935261     0.90   0.372    -.4288031    1.134814
        221  |   .5906457   .3513669     1.68   0.096     -.107406    1.288697
        227  |  -.1051563   .1095011    -0.96   0.339    -.3226993    .1123868
        231  |  -.0672952   .1038438    -0.65   0.519    -.2735991    .1390087
        233  |   -.021184   .0976363    -0.22   0.829    -.2151556    .1727876
        235  |  -.0806843    .096643    -0.83   0.406    -.2726825    .1113139
        237  |    .973278   .1151159     8.45   0.000     .7445802    1.201976
        239  |   .9640105   .0956111    10.08   0.000     .7740623    1.153959
        241  |  -.0451428   .0726478    -0.62   0.536    -.1894704    .0991848
        243  |  -.3096883   .1145459    -2.70   0.008    -.5372537   -.0821229
        244  |  -.0805531   .0695285    -1.16   0.250    -.2186836    .0575775
        245  |  -.0451428   .0726478    -0.62   0.536    -.1894704    .0991848
        247  |   .4613498   .2840257     1.62   0.108    -.1029168    1.025616
        248  |  -.0791206   .0702223    -1.13   0.263    -.2186294    .0603882
        250  |  -.1424639   .1072858    -1.33   0.188    -.3556059    .0706781
        251  |    .083694    .299493     0.28   0.781     -.511301    .6786891
        252  |  -.0518874   .0669685    -0.77   0.440    -.1849321    .0811572
        267  |  -.1239015   .0826909    -1.50   0.138    -.2881814    .0403784
        269  |  -.0451428   .0726478    -0.62   0.536    -.1894704    .0991848
        271  |  -.3125082   .1611569    -1.94   0.056    -.6326746    .0076582
        272  |  -.1438758   .0868932    -1.66   0.101    -.3165042    .0287527
        274  |  -.1424639   .1072858    -1.33   0.188    -.3556059    .0706781
        275  |  -.2071968   .1551528    -1.34   0.185    -.5154349    .1010412
        276  |  -.0805531   .0695285    -1.16   0.250    -.2186836    .0575775
        278  |  -.1424639   .1072858    -1.33   0.188    -.3556059    .0706781
        279  |  -.1571204     .11033    -1.42   0.158    -.3763101    .0620694
        280  |  -.0518874   .0669685    -0.77   0.440    -.1849321    .0811572
        283  |   -.067997   .0933427    -0.73   0.468    -.2534385    .1174445
        284  |   .8589892   .0896293     9.58   0.000      .680925    1.037053
        285  |  -.1342662   .0949488    -1.41   0.161    -.3228985    .0543662
        287  |  -.1239015   .0826909    -1.50   0.138    -.2881814    .0403784
             |
      fYes_T |   .0891233   .0541565     1.65   0.103     -.018468    .1967147
        mage |  -.0038796   .0032314    -1.20   0.233    -.0102993      .00254
    mmarried |   .0184442   .0708664     0.26   0.795    -.1223442    .1592327
       makan |  -.0642548   .0628405    -1.02   0.309    -.1890985    .0605889
mselfemplo~d |  -.0846515   .0507356    -1.67   0.099    -.1854467    .0161437
       m2q1a |   .0126535   .0173358     0.73   0.467    -.0217871    .0470942
      2.m3q1 |  -.0406722   .0723543    -0.56   0.575    -.1844167    .1030723
         trt |  -.2110939   .0863031    -2.45   0.016      -.38255   -.0396378
       _cons |   .3911605   .1202372     3.25   0.002     .1522883    .6300327
------------------------------------------------------------------------------

. reg fd i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a i.m3
> q1 trt2 trt3 trt4, r cluster(uniqueVendorID) level(95) //only rep vendors

Linear regression                               Number of obs     =        335
                                                F(75, 90)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6876
                                                Root MSE          =     .29258

                        (Std. err. adjusted for 91 clusters in uniqueVendorID)
------------------------------------------------------------------------------
             |               Robust
          fd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.1720881   .1223245    -1.41   0.163    -.4151071    .0709308
          5  |   .4065839   .3831034     1.06   0.291    -.3545178    1.167686
          6  |  -.0451877   .1625648    -0.28   0.782    -.3681511    .2777757
          7  |  -.1720881   .1223245    -1.41   0.163    -.4151071    .0709308
          8  |   .0127877   .0788603     0.16   0.872     -.143882    .1694574
         11  |   .0228261   .0979286     0.23   0.816    -.1717261    .2173784
         14  |   .0228261   .0979286     0.23   0.816    -.1717261    .2173784
         18  |  -.0451877   .1625648    -0.28   0.782    -.3681511    .2777757
         22  |  -.1720881   .1223245    -1.41   0.163    -.4151071    .0709308
         23  |   .0008988   .0850567     0.01   0.992    -.1680812    .1698789
         24  |  -.0451877   .1625648    -0.28   0.782    -.3681511    .2777757
         26  |  -.0747341   .1393126    -0.54   0.593     -.351503    .2020347
         27  |  -.0451877   .1625648    -0.28   0.782    -.3681511    .2777757
         29  |  -.0861459   .1393614    -0.62   0.538    -.3630117      .19072
         30  |  -.0451877   .1625648    -0.28   0.782    -.3681511    .2777757
         32  |   .1149532   .2083837     0.55   0.583    -.2990374    .5289439
         33  |  -.0451877   .1625648    -0.28   0.782    -.3681511    .2777757
         34  |  -.1720881   .1223245    -1.41   0.163    -.4151071    .0709308
         35  |     -.0185    .070421    -0.26   0.793    -.1584036    .1214037
         37  |  -.0795245   .0863999    -0.92   0.360    -.2511729    .0921239
         38  |  -.0011219   .1592164    -0.01   0.994     -.317433    .3151893
         39  |  -.0795102   .0856801    -0.93   0.356    -.2497287    .0907084
         40  |   .2021132   .2231855     0.91   0.368    -.2412838    .6455103
         41  |   .0598904   .2101378     0.29   0.776    -.3575849    .4773658
         42  |   .3120794   .2709937     1.15   0.253    -.2262968    .8504557
         51  |  -.0926029   .0809984    -1.14   0.256    -.2535204    .0683146
         52  |  -.2089177   .1283851    -1.63   0.107     -.463977    .0461416
         53  |  -.0869915   .0923629    -0.94   0.349    -.2704864    .0965034
         54  |  -.0301392   .1443942    -0.21   0.835    -.3170034     .256725
         55  |  -.0745278   .0976788    -0.76   0.447    -.2685837    .1195281
         56  |  -.0008763   .1221959    -0.01   0.994    -.2436398    .2418873
         57  |   .8308642    .095306     8.72   0.000     .6415223    1.020206
         58  |   .8300699   .1762097     4.71   0.000     .4799986    1.180141
         61  |  -.0652686   .1076721    -0.61   0.546     -.279178    .1486408
         62  |  -.1204084   .0920557    -1.31   0.194    -.3032931    .0624763
         63  |  -.1133814    .130315    -0.87   0.387    -.3722749    .1455121
         64  |  -.2580322   .1173827    -2.20   0.030    -.4912335   -.0248309
         65  |   .0158098   .1441114     0.11   0.913    -.2704928    .3021123
         66  |   .8411558   .1263273     6.66   0.000     .5901846    1.092127
         67  |   .4761427   .2323769     2.05   0.043     .0144854    .9377999
         68  |  -.2580322   .1173827    -2.20   0.030    -.4912335   -.0248309
         69  |   .0717065   .1226236     0.58   0.560    -.1719067    .3153198
         71  |   .3186749   .2533904     1.26   0.212    -.1847293    .8220792
         72  |  -.2580322   .1173827    -2.20   0.030    -.4912335   -.0248309
         89  |   .0372121   .1141837     0.33   0.745    -.1896337    .2640579
         90  |  -.1370349   .1184461    -1.16   0.250    -.3723487    .0982789
         91  |  -.1735912   .1655894    -1.05   0.297    -.5025635    .1553811
         93  |   .0619154   .0976845     0.63   0.528     -.132152    .2559828
         94  |  -.2027385   .1396945    -1.45   0.150    -.4802661     .074789
         95  |  -.1738935   .1425674    -1.22   0.226    -.4571285    .1093416
         97  |  -.0204617   .0947299    -0.22   0.829    -.2086593    .1677358
         98  |  -.1204084   .0920557    -1.31   0.194    -.3032931    .0624763
         99  |  -.1407087   .1582444    -0.89   0.376    -.4550887    .1736714
        102  |   .4016768   .5797787     0.69   0.490    -.7501549    1.553508
        103  |   1.057077   .0883545    11.96   0.000     .8815455    1.232609
        104  |    .654179   .1403923     4.66   0.000     .3752651    .9330929
        107  |   .0132359    .097224     0.14   0.892    -.1799166    .2063884
        109  |  -.1638366   .1164253    -1.41   0.163    -.3951358    .0674627
        110  |  -.0760628    .099869    -0.76   0.448    -.2744699    .1223444
        113  |  -.0923236   .0927036    -1.00   0.322    -.2764955    .0918483
        114  |  -.1795011   .1792709    -1.00   0.319    -.5356539    .1766518
        117  |  -.0923236   .0927036    -1.00   0.322    -.2764955    .0918483
        118  |   -.205686   .1056661    -1.95   0.055    -.4156102    .0042382
        137  |  -.1801123   .1046399    -1.72   0.089    -.3879978    .0277731
        138  |  -.1530189   .1082637    -1.41   0.161    -.3681035    .0620658
        141  |  -.2353496   .1110706    -2.12   0.037    -.4560107   -.0146885
        142  |  -.2132201   .1141509    -1.87   0.065    -.4400007    .0135605
        145  |  -.1475608    .102312    -1.44   0.153    -.3508214    .0556997
        146  |  -.1599085   .1085612    -1.47   0.144    -.3755842    .0557672
        149  |   .3800578    .472897     0.80   0.424    -.5594347     1.31955
        150  |   .8495581   .1388102     6.12   0.000     .5737874    1.125329
        153  |  -.0923236   .0927036    -1.00   0.322    -.2764955    .0918483
        154  |  -.0275626   .1236176    -0.22   0.824    -.2731506    .2180254
        157  |  -.0870981   .1225964    -0.71   0.479    -.3306572    .1564611
        158  |  -.0042501   .1722901    -0.02   0.980    -.3465344    .3380343
        159  |  -.0129404   .0861864    -0.15   0.881    -.1841648     .158284
        160  |   .8828688   .1494761     5.91   0.000     .5859085    1.179829
        162  |   .8828688   .1494761     5.91   0.000     .5859085    1.179829
        171  |  -.1612557   .1487806    -1.08   0.281    -.4568344     .134323
        172  |   .1086311   .1328292     0.82   0.416    -.1552573    .3725195
        173  |  -.1612557   .1487806    -1.08   0.281    -.4568344     .134323
        174  |  -.1171312   .1494761    -0.78   0.435    -.4140915     .179829
        175  |  -.0870981   .1225964    -0.71   0.479    -.3306572    .1564611
        176  |  -.1171312   .1494761    -0.78   0.435    -.4140915     .179829
        177  |   .8992708   .1002585     8.97   0.000     .7000899    1.098452
        178  |   .4957499   .4024777     1.23   0.221    -.3038423    1.295342
        180  |   .4957499   .4024777     1.23   0.221    -.3038423    1.295342
        181  |  -.2271411   .1330453    -1.71   0.091    -.4914589    .0371767
        182  |  -.0425617   .1042074    -0.41   0.684    -.2495879    .1644644
        183  |   .8364833   .1161987     7.20   0.000     .6056344    1.067332
        184  |   .2343429   .4003746     0.59   0.560    -.5610712    1.029757
        185  |  -.2209592   .1185688    -1.86   0.066    -.4565167    .0145984
        186  |  -.0306343   .1209722    -0.25   0.801    -.2709666    .2096981
        187  |  -.1635167   .1161987    -1.41   0.163    -.3943656    .0673323
        189  |  -.1635167   .1161987    -1.41   0.163    -.3943656    .0673323
        191  |  -.1635167   .1161987    -1.41   0.163    -.3943656    .0673323
        193  |  -.1635167   .1161987    -1.41   0.163    -.3943656    .0673323
        195  |  -.1984199   .1134156    -1.75   0.084    -.4237398    .0269001
        196  |   .4560307   .4320452     1.06   0.294    -.4023025    1.314364
        197  |   .1566633   .4195311     0.37   0.710    -.6768085    .9901351
        198  |  -.1133586   .1019441    -1.11   0.269    -.3158883    .0891711
        199  |    -.17667   .0987305    -1.79   0.077    -.3728153    .0194752
        200  |  -.0602609   .0966143    -0.62   0.534     -.252202    .1316801
        201  |   .8077621    .102283     7.90   0.000      .604559    1.010965
        202  |   .9744302   .0796582    12.23   0.000     .8161754    1.132685
        203  |   .8364833   .1161987     7.20   0.000     .6056344    1.067332
        204  |  -.0664166   .1035496    -0.64   0.523    -.2721359    .1393027
        205  |  -.0698484   .0992254    -0.70   0.483     -.266977    .1272801
        206  |   .4556583   .4253978     1.07   0.287    -.3894685    1.300785
        207  |   .1733344   .2714468     0.64   0.525     -.365942    .7126108
        210  |  -.0811929   .1011052    -0.80   0.424    -.2820559    .1196702
        211  |  -.1397187   .1042391    -1.34   0.183    -.3468079    .0673704
        212  |  -.1142549   .1003154    -1.14   0.258    -.3135488    .0850391
        213  |  -.0931099   .1315138    -0.71   0.481    -.3543851    .1681653
        214  |  -.1431088   .1426948    -1.00   0.319    -.4265969    .1403794
        215  |   .8857451   .1003154     8.83   0.000     .6864512    1.085039
        216  |     .44468   .4194746     1.06   0.292    -.3886795     1.27804
        217  |   .3062412    .360535     0.85   0.398    -.4100246    1.022507
        219  |   .3561232   .3965357     0.90   0.372    -.4316641    1.143911
        221  |   .5878637   .3483942     1.69   0.095    -.1042822    1.280009
        227  |  -.0942547   .1226639    -0.77   0.444     -.337948    .1494387
        231  |  -.0563551   .1108997    -0.51   0.613    -.2766766    .1639665
        233  |   -.020136   .1065086    -0.19   0.850     -.231734     .191462
        235  |  -.0765606   .1049232    -0.73   0.467    -.2850089    .1318876
        237  |   .9711822   .1264186     7.68   0.000     .7200295    1.222335
        239  |    .960074   .1095022     8.77   0.000     .7425287    1.177619
        241  |  -.0360592   .0877854    -0.41   0.682    -.2104603    .1383419
        243  |  -.3039526   .1198728    -2.54   0.013    -.5421007   -.0658044
        244  |  -.0831659   .0890589    -0.93   0.353    -.2600969    .0937651
        245  |  -.0360592   .0877854    -0.41   0.682    -.2104603    .1383419
        247  |   .4685145   .2855657     1.64   0.104    -.0988116    1.035841
        248  |      -.098   .0903276    -1.08   0.281    -.2774515    .0814516
        250  |  -.1351809   .1164517    -1.16   0.249    -.3665325    .0961707
        251  |   .0907317   .3043854     0.30   0.766    -.5139829    .6954464
        252  |  -.0693651   .0847938    -0.82   0.415    -.2378229    .0990926
        267  |  -.1170327   .0952595    -1.23   0.222    -.3062824     .072217
        269  |  -.0360592   .0877854    -0.41   0.682    -.2104603    .1383419
        271  |  -.3053861   .1657339    -1.84   0.069    -.6346454    .0238733
        272  |  -.1274858   .1027841    -1.24   0.218    -.3316843    .0767128
        274  |  -.1351809   .1164517    -1.16   0.249    -.3665325    .0961707
        275  |  -.1991078    .160905    -1.24   0.219    -.5187737    .1205581
        276  |  -.0831659   .0890589    -0.93   0.353    -.2600969    .0937651
        278  |  -.1351809   .1164517    -1.16   0.249    -.3665325    .0961707
        279  |   -.147147   .1194219    -1.23   0.221    -.3843993    .0901054
        280  |  -.0693651   .0847938    -0.82   0.415    -.2378229    .0990926
        283  |  -.0593582   .1018445    -0.58   0.561      -.26169    .1429736
        284  |   .8428461   .0988696     8.52   0.000     .6464243    1.039268
        285  |   -.123848   .1089731    -1.14   0.259     -.340342    .0926461
        287  |  -.1170327   .0952595    -1.23   0.222    -.3062824     .072217
             |
      fYes_T |   .0877888   .0550661     1.59   0.114    -.0216097    .1971872
        mage |  -.0037831   .0033504    -1.13   0.262    -.0104393    .0028731
    mmarried |   .0189093   .0730775     0.26   0.796    -.1262719    .1640906
       makan |  -.0668464   .0642069    -1.04   0.301    -.1944047    .0607119
mselfemplo~d |  -.0831663   .0508262    -1.64   0.105    -.1841414    .0178089
       m2q1a |   .0123546   .0170696     0.72   0.471    -.0215572    .0462663
      2.m3q1 |  -.0426023   .0753184    -0.57   0.573    -.1922355    .1070309
        trt2 |   -.184884    .094593    -1.95   0.054    -.3728094    .0030415
        trt3 |    -.21733   .0938898    -2.31   0.023    -.4038585   -.0308015
        trt4 |   -.211629   .0898374    -2.36   0.021    -.3901067   -.0331513
       _cons |   .3833977   .1257925     3.05   0.003     .1334889    .6333065
------------------------------------------------------------------------------

. test _b[trt2]=_b[trt4]

 ( 1)  trt2 - trt4 = 0

       F(  1,    90) =    0.18
            Prob > F =    0.6705

. test _b[trt3]=_b[trt4]

 ( 1)  trt3 - trt4 = 0

       F(  1,    90) =    0.01
            Prob > F =    0.9210

. test _b[trt2]=_b[trt3]

 ( 1)  trt2 - trt3 = 0

       F(  1,    90) =    0.34
            Prob > F =    0.5631

. test _b[trt2] + _b[trt3] =_b[trt4]

 ( 1)  trt2 + trt3 - trt4 = 0

       F(  1,    90) =    2.64
            Prob > F =    0.1080

. 
. reg fdamt i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a i
> .m3q1 trt, r cluster(uniqueVendorID) level(95) //only rep vendors

Linear regression                               Number of obs     =        335
                                                F(74, 90)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6626
                                                Root MSE          =     .98339

                        (Std. err. adjusted for 91 clusters in uniqueVendorID)
------------------------------------------------------------------------------
             |               Robust
       fdamt | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.4582819   .3229791    -1.42   0.159    -1.099936    .1833726
          5  |   1.897675   1.555242     1.22   0.226    -1.192084    4.987435
          6  |  -.2000665   .4646267    -0.43   0.668    -1.123129    .7229956
          7  |  -.4582819   .3229791    -1.42   0.159    -1.099936    .1833726
          8  |  -.0708589    .233535    -0.30   0.762    -.5348169    .3930992
         11  |  -.0642205   .2859008    -0.22   0.823    -.6322123    .5037713
         14  |  -.0642205   .2859008    -0.22   0.823    -.6322123    .5037713
         18  |  -.2000665   .4646267    -0.43   0.668    -1.123129    .7229956
         22  |  -.4582819   .3229791    -1.42   0.159    -1.099936    .1833726
         23  |  -.0754331   .2259705    -0.33   0.739    -.5243629    .3734966
         24  |  -.2000665   .4646267    -0.43   0.668    -1.123129    .7229956
         26  |  -.3086423   .3715363    -0.83   0.408    -1.046764    .4294794
         27  |  -.2000665   .4646267    -0.43   0.668    -1.123129    .7229956
         29  |   -.300955   .3771679    -0.80   0.427    -1.050265    .4483549
         30  |  -.2000665   .4646267    -0.43   0.668    -1.123129    .7229956
         32  |  -.0615683   .3466519    -0.18   0.859    -.7502528    .6271161
         33  |  -.2000665   .4646267    -0.43   0.668    -1.123129    .7229956
         34  |  -.4582819   .3229791    -1.42   0.159    -1.099936    .1833726
         35  |  -.1563198    .223856    -0.70   0.487    -.6010488    .2884093
         37  |  -.2914048   .2623388    -1.11   0.270    -.8125866    .2297769
         38  |    .221679   .7246807     0.31   0.760    -1.218026    1.661384
         39  |  -.2463991   .2505101    -0.98   0.328    -.7440812     .251283
         40  |   .8306982   .8672168     0.96   0.341    -.8921794    2.553576
         41  |  -.2246839   .3130756    -0.72   0.475    -.8466632    .3972955
         42  |  -.1586843   .4071062    -0.39   0.698    -.9674719    .6501033
         51  |   -.329452   .2462026    -1.34   0.184    -.8185765    .1596724
         52  |   -.728565   .3803086    -1.92   0.059    -1.484114    .0269844
         53  |  -.3135352   .2612306    -1.20   0.233    -.8325154    .2054451
         54  |  -.2224303   .4650219    -0.48   0.634    -1.146277    .7014169
         55  |  -.2368471   .3103093    -0.76   0.447    -.8533307    .3796366
         56  |  -.1051616   .3592993    -0.29   0.770    -.8189723     .608649
         57  |   1.371158   1.054409     1.30   0.197    -.7236094    3.465926
         58  |   .4256379   .5113153     0.83   0.407    -.5901791    1.441455
         61  |  -.3190787   .3348752    -0.95   0.343    -.9843667    .3462093
         62  |  -.4109146   .2662028    -1.54   0.126    -.9397729    .1179437
         63  |  -.4230628   .3757698    -1.13   0.263    -1.169595    .3234694
         64  |  -.7800383   .3747307    -2.08   0.040    -1.524506   -.0355704
         65  |  -.0330798   .4661105    -0.07   0.944    -.9590897    .8929302
         66  |   3.552302   .3817807     9.30   0.000     2.793828    4.310776
         67  |   1.970753   .9958081     1.98   0.051    -.0075936      3.9491
         68  |  -.7800383   .3747307    -2.08   0.040    -1.524506   -.0355704
         69  |   .1140412   .3413269     0.33   0.739    -.5640643    .7921466
         71  |   .1235593    .291728     0.42   0.673    -.4560092    .7031279
         72  |  -.7800383   .3747307    -2.08   0.040    -1.524506   -.0355704
         89  |   .0549452   .3369338     0.16   0.871    -.6144325    .7243229
         90  |  -.3361518   .3194161    -1.05   0.295    -.9707277     .298424
         91  |   -.586276   .5050997    -1.16   0.249    -1.589745    .4171927
         93  |   .1059689   .2973785     0.36   0.722    -.4848253    .6967632
         94  |  -.6236008   .4062771    -1.53   0.128    -1.430741    .1835395
         95  |  -.6345662   .4187831    -1.52   0.133    -1.466552    .1974195
         97  |  -.1755244   .2666284    -0.66   0.512    -.7052284    .3541795
         98  |  -.4109146   .2662028    -1.54   0.126    -.9397729    .1179437
         99  |  -.5130443   .4516401    -1.14   0.259    -1.410306    .3842176
        102  |   .1534424   .7554691     0.20   0.840    -1.347429    1.654314
        103  |   1.155556   .2369747     4.88   0.000      .684764    1.626347
        104  |  -.1318443   .4422878    -0.30   0.766    -1.010526    .7468375
        107  |  -.0390994    .289731    -0.13   0.893    -.6147005    .5365017
        109  |  -.5561641   .3579146    -1.55   0.124    -1.267224    .1548958
        110  |  -.2787964   .2976886    -0.94   0.352    -.8702068    .3126141
        113  |  -.3262379   .2677346    -1.22   0.226    -.8581395    .2056636
        114  |  -.5203495   .5462577    -0.95   0.343    -1.605586    .5648867
        117  |  -.3262379   .2677346    -1.22   0.226    -.8581395    .2056636
        118  |  -.6368641    .327086    -1.95   0.055    -1.286678    .0129494
        137  |  -.6780439   .3072037    -2.21   0.030    -1.288358   -.0677301
        138  |  -.4881607   .3100566    -1.57   0.119    -1.104142    .1278209
        141  |  -.7860902   .3320658    -2.37   0.020    -1.445797   -.1263834
        142  |  -.5319197   .3389904    -1.57   0.120    -1.205383     .141544
        145  |  -.4342842   .2745476    -1.58   0.117     -.979721    .1111525
        146  |  -.5229768   .2976814    -1.76   0.082    -1.114373    .0684193
        149  |   .6197389   .9616428     0.64   0.521    -1.290732     2.53021
        150  |   .5443544   .4200282     1.30   0.198    -.2901049    1.378814
        153  |  -.3262379   .2677346    -1.22   0.226    -.8581395    .2056636
        154  |  -.0665935   .3556449    -0.19   0.852    -.7731442    .6399573
        157  |  -.2266588   .3800189    -0.60   0.552    -.9816328    .5283151
        158  |  -.0664961    .442106    -0.15   0.881    -.9448169    .8118246
        159  |  -.0185652   .2419088    -0.08   0.939    -.4991593     .462029
        160  |   3.671584   .3878624     9.47   0.000     2.901027     4.44214
        162  |   .6715838   .3878624     1.73   0.087    -.0989727     1.44214
        171  |  -.4347525   .5148858    -0.84   0.401    -1.457663     .588158
        172  |    .195424   .3855605     0.51   0.613    -.5705592    .9614072
        173  |  -.4347525   .5148858    -0.84   0.401    -1.457663     .588158
        174  |  -.3284162   .3878624    -0.85   0.399    -1.098973    .4421402
        175  |  -.2266588   .3800189    -0.60   0.552    -.9816328    .5283151
        176  |  -.3284162   .3878624    -0.85   0.399    -1.098973    .4421402
        177  |   .6296289   .2968852     2.12   0.037     .0398147    1.219443
        178  |   .4335039   .4290525     1.01   0.315    -.4188839    1.285892
        180  |   1.433504   1.261872     1.14   0.259    -1.073426    3.940434
        181  |  -.7591485   .4076957    -1.86   0.066    -1.569107    .0508103
        182  |  -.2596014    .320659    -0.81   0.420    -.8966465    .3774438
        183  |   3.577167   .3095292    11.56   0.000     2.962233    4.192101
        184  |   .8946208   1.553752     0.58   0.566    -2.192178     3.98142
        185  |  -.8339218   .3532763    -2.36   0.020    -1.535767   -.1320767
        186  |  -.2214619   .3981064    -0.56   0.579     -1.01237    .5694459
        187  |  -.4228333   .3095292    -1.37   0.175    -1.037767    .1921005
        189  |  -.4228333   .3095292    -1.37   0.175    -1.037767    .1921005
        191  |  -.4228333   .3095292    -1.37   0.175    -1.037767    .1921005
        193  |  -.4228333   .3095292    -1.37   0.175    -1.037767    .1921005
        195  |  -.5536042   .3385263    -1.64   0.105    -1.226146    .1189373
        196  |   2.228025   2.202446     1.01   0.314    -2.147518    6.603568
        197  |  -.1964414   .4987467    -0.39   0.695    -1.187289     .794406
        198  |  -.5318083   .2906108    -1.83   0.071    -1.109157    .0455408
        199  |  -.5297748   .2856395    -1.85   0.067    -1.097247    .0376979
        200  |  -.3276531   .2952022    -1.11   0.270    -.9141239    .2588177
        201  |   2.371622   .3429724     6.91   0.000     1.690248    3.052997
        202  |   4.819998   .2315877    20.81   0.000     4.359908    5.280087
        203  |   4.577167   .3095292    14.79   0.000     3.962233    5.192101
        204  |  -.3358803   .3078466    -1.09   0.278    -.9474712    .2757107
        205  |  -.2357256   .2986552    -0.79   0.432    -.8290563    .3576051
        206  |    1.78307   1.736049     1.03   0.307    -1.665895    5.232035
        207  |   .8997996   1.363551     0.66   0.511    -1.809133    3.608732
        210  |  -.2656836   .3050219    -0.87   0.386    -.8716628    .3402955
        211  |  -.5002209   .3430505    -1.46   0.148    -1.181751    .1813088
        212  |  -.4202005   .2879737    -1.46   0.148    -.9923104    .1519094
        213  |  -.3647074   .3911364    -0.93   0.354    -1.141768    .4123534
        214  |  -.5572034   .4121332    -1.35   0.180    -1.375978    .2615711
        215  |   .5797995   .2879737     2.01   0.047     .0076896    1.151909
        216  |   1.294603   1.276134     1.01   0.313     -1.24066    3.829866
        217  |   1.438321   1.783898     0.81   0.422    -2.105704    4.982345
        219  |   1.428266   1.523307     0.94   0.351    -1.598049     4.45458
        221  |   .3928598   .4425725     0.89   0.377    -.4863878    1.272107
        227  |  -.4555486   .3750922    -1.21   0.228    -1.200735    .2896375
        231  |  -.3456147   .3641812    -0.95   0.345    -1.069124    .3778947
        233  |  -.1631236   .3199741    -0.51   0.611    -.7988081    .4725609
        235  |  -.3788297   .3220132    -1.18   0.243    -1.018565    .2609056
        237  |   1.824049   .9573342     1.91   0.060    -.0778622    3.725961
        239  |   5.818188   1.100087     5.29   0.000     3.632673    8.003703
        241  |  -.2030678    .248323    -0.82   0.416    -.6964048    .2902691
        243  |  -.8767416   .4021688    -2.18   0.032     -1.67572   -.0777631
        244  |  -.2849281   .2328044    -1.22   0.224    -.7474348    .1775785
        245  |  -.2030678    .248323    -0.82   0.416    -.6964048    .2902691
        247  |   2.093628   1.249934     1.67   0.097    -.3895828     4.57684
        248  |  -.2840778   .2335016    -1.22   0.227    -.7479695    .1798139
        250  |  -.5034807   .3304451    -1.52   0.131    -1.159968    .1530063
        251  |  -.4269928   .3452065    -1.24   0.219    -1.112806    .2588202
        252  |  -.2171516   .2349069    -0.92   0.358    -.6838351    .2495319
        267  |  -.4219012   .2740136    -1.54   0.127    -.9662769    .1224746
        269  |  -.2030678    .248323    -0.82   0.416    -.6964048    .2902691
        271  |  -.9295387   .5040962    -1.84   0.068    -1.931014    .0719365
        272  |  -.5706582   .2888801    -1.98   0.051    -1.144569    .0032525
        274  |  -.5034807   .3304451    -1.52   0.131    -1.159968    .1530063
        275  |  -.6082363   .4840486    -1.26   0.212    -1.569883    .3534107
        276  |  -.2849281   .2328044    -1.22   0.224    -.7474348    .1775785
        278  |  -.5034807   .3304451    -1.52   0.131    -1.159968    .1530063
        279  |  -.5703385   .3687316    -1.55   0.125    -1.302888    .1622112
        280  |  -.2171516   .2349069    -0.92   0.358    -.6838351    .2495319
        283  |  -.2185325   .3151285    -0.69   0.490    -.8445902    .4075252
        284  |   .4310425   .2969736     1.45   0.150    -.1589474    1.021032
        285  |  -.5548738   .3110632    -1.78   0.078    -1.172855    .0631076
        287  |  -.4219012   .2740136    -1.54   0.127    -.9662769    .1224746
             |
      fYes_T |    .351806   .1764955     1.99   0.049     .0011668    .7024452
        mage |  -.0123831   .0108572    -1.14   0.257    -.0339527    .0091866
    mmarried |   .1274287    .241443     0.53   0.599      -.35224    .6070973
       makan |  -.1799835   .2078536    -0.87   0.389    -.5929208    .2329539
mselfemplo~d |  -.3200558   .1889673    -1.69   0.094    -.6954724    .0553607
       m2q1a |   .0478316   .0471618     1.01   0.313    -.0458635    .1415267
      2.m3q1 |   -.082034   .2270069    -0.36   0.719    -.5330228    .3689548
         trt |  -.5505037   .2559821    -2.15   0.034    -1.059057   -.0419505
       _cons |   1.136786   .4283718     2.65   0.009     .2857508    1.987821
------------------------------------------------------------------------------

. reg fdamt i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a i
> .m3q1 trt2 trt3 trt4, r cluster(uniqueVendorID) level(95) //rep vendors

Linear regression                               Number of obs     =        335
                                                F(76, 90)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6634
                                                Root MSE          =     .98782

                        (Std. err. adjusted for 91 clusters in uniqueVendorID)
------------------------------------------------------------------------------
             |               Robust
       fdamt | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.4269911   .3471543    -1.23   0.222    -1.116674    .2626915
          5  |   1.928633   1.577254     1.22   0.225    -1.204857    5.062124
          6  |  -.2788827   .4899852    -0.57   0.571    -1.252324    .6945584
          7  |  -.4269911   .3471543    -1.23   0.222    -1.116674    .2626915
          8  |  -.0189659   .2613268    -0.07   0.942    -.5381373    .5002054
         11  |   .0040794    .316513     0.01   0.990    -.6247289    .6328876
         14  |   .0040794    .316513     0.01   0.990    -.6247289    .6328876
         18  |  -.2788827   .4899852    -0.57   0.571    -1.252324    .6945584
         22  |  -.4269911   .3471543    -1.23   0.222    -1.116674    .2626915
         23  |  -.0335199   .2849248    -0.12   0.907    -.5995728     .532533
         24  |  -.2788827   .4899852    -0.57   0.571    -1.252324    .6945584
         26  |  -.2597124   .4031631    -0.64   0.521    -1.060666    .5412416
         27  |  -.2788827   .4899852    -0.57   0.571    -1.252324    .6945584
         29  |  -.2608157    .400951    -0.65   0.517    -1.057375    .5357435
         30  |  -.2788827   .4899852    -0.57   0.571    -1.252324    .6945584
         32  |  -.0118559   .3763098    -0.03   0.975     -.759461    .7357492
         33  |  -.2788827   .4899852    -0.57   0.571    -1.252324    .6945584
         34  |  -.4269911   .3471543    -1.23   0.222    -1.116674    .2626915
         35  |   -.142026   .2497638    -0.57   0.571    -.6382254    .3541735
         37  |  -.2998468   .2931662    -1.02   0.309    -.8822727    .2825792
         38  |   .2408533   .7456581     0.32   0.747    -1.240527    1.722233
         39  |  -.2844996     .28785    -0.99   0.326    -.8563639    .2873647
         40  |   .8709117    .881873     0.99   0.326    -.8810829    2.622906
         41  |  -.2288246   .3426407    -0.67   0.506    -.9095403     .451891
         42  |  -.1177637   .4299638    -0.27   0.785    -.9719619    .7364345
         51  |  -.3276455    .281603    -1.16   0.248    -.8870989     .231808
         52  |   -.685703   .4374666    -1.57   0.121    -1.554807    .1834007
         53  |  -.3605983   .3012748    -1.20   0.234    -.9591333    .2379367
         54  |  -.1727535    .482066    -0.36   0.721    -1.130462    .7849547
         55  |   -.258522     .32386    -0.80   0.427    -.9019264    .3848825
         56  |  -.0573906   .3839516    -0.15   0.882    -.8201775    .7053962
         57  |   1.353872   1.023357     1.32   0.189    -.6792056    3.386949
         58  |    .465996   .5547713     0.84   0.403     -.636154    1.568146
         61  |  -.2705503   .3654301    -0.74   0.461    -.9965409    .4554404
         62  |  -.3645668   .2986433    -1.22   0.225    -.9578738    .2287403
         63  |  -.4159627    .410163    -1.01   0.313    -1.230823    .3988977
         64  |  -.7425862    .394333    -1.88   0.063    -1.525997     .040825
         65  |   .0023549   .4809864     0.00   0.996    -.9532085    .9579183
         66  |   3.579952    .398136     8.99   0.000     2.788985    4.370919
         67  |    2.00912   1.002058     2.00   0.048      .018358    3.999883
         68  |  -.7425862    .394333    -1.88   0.063    -1.525997     .040825
         69  |   .1800416   .3810966     0.47   0.638    -.5770734    .9371566
         71  |   .1152452   .3287538     0.35   0.727    -.5378816    .7683719
         72  |  -.7425862    .394333    -1.88   0.063    -1.525997     .040825
         89  |   .1020436   .3722135     0.27   0.785    -.6374236    .8415107
         90  |  -.3083842   .3405723    -0.91   0.368    -.9849906    .3682222
         91  |  -.5881867   .5116931    -1.15   0.253    -1.604754    .4283809
         93  |   .1550407   .3253991     0.48   0.635    -.4914213    .8015027
         94  |  -.5930923   .4205545    -1.41   0.162    -1.428597    .2424126
         95  |  -.5938275   .4446259    -1.34   0.185    -1.477154    .2894994
         97  |   -.129269   .3030459    -0.43   0.671    -.7313226    .4727846
         98  |  -.3645668   .2986433    -1.22   0.225    -.9578738    .2287403
         99  |  -.4719364   .4721622    -1.00   0.320    -1.409969    .4660961
        102  |   .1968137    .789202     0.25   0.804    -1.371074    1.764701
        103  |   1.135299   .2929912     3.87   0.000     .5532214    1.717378
        104  |  -.0886749   .4604395    -0.19   0.848    -1.003418    .8260685
        107  |   -.034553   .3436316    -0.10   0.920    -.7172371    .6481311
        109  |  -.5813866   .4228803    -1.37   0.173    -1.421512    .2587389
        110  |  -.2711185    .329638    -0.82   0.413    -.9260019    .3837648
        113  |  -.2979966   .3229888    -0.92   0.359    -.9396703    .3436771
        114  |  -.5561836   .5280782    -1.05   0.295    -1.605303    .4929361
        117  |  -.2979966   .3229888    -0.92   0.359    -.9396703    .3436771
        118  |  -.6048832   .3592382    -1.68   0.096    -1.318573    .1088061
        137  |  -.6440853   .3537228    -1.82   0.072    -1.346817    .0586469
        138  |   -.495564   .3510006    -1.41   0.161    -1.192888      .20176
        141  |  -.8647766   .3730828    -2.32   0.023    -1.605971   -.1235825
        142  |  -.5144767   .3672367    -1.40   0.165    -1.244056    .2151031
        145  |   -.518688   .3318634    -1.56   0.122    -1.177993    .1406167
        146  |   -.488688    .332391    -1.47   0.145    -1.149041    .1716648
        149  |   .5916577   .9276369     0.64   0.525    -1.251255     2.43457
        150  |   .5298788   .4268099     1.24   0.218    -.3180535    1.377811
        153  |  -.2979966   .3229888    -0.92   0.359    -.9396703    .3436771
        154  |  -.1651858   .4177026    -0.40   0.693    -.9950249    .6646532
        157  |  -.2114699   .4033696    -0.52   0.601    -1.012834    .5898941
        158  |   -.028726   .4573281    -0.06   0.950    -.9372881    .8798361
        159  |   .0040907    .304114     0.01   0.989    -.6000848    .6082662
        160  |   3.710304   .4081003     9.09   0.000     2.899541    4.521066
        162  |   .7103037   .4081003     1.74   0.085    -.1004588    1.521066
        171  |  -.4270305   .5286704    -0.81   0.421    -1.477326    .6232654
        172  |   .2322442   .3947787     0.59   0.558    -.5520526    1.016541
        173  |  -.4270305   .5286704    -0.81   0.421    -1.477326    .6232654
        174  |  -.2896963   .4081003    -0.71   0.480    -1.100459    .5210663
        175  |  -.2114699   .4033696    -0.52   0.601    -1.012834    .5898941
        176  |  -.2896963   .4081003    -0.71   0.480    -1.100459    .5210663
        177  |    .658002    .345744     1.90   0.060    -.0288788    1.344883
        178  |    .471274   .4456051     1.06   0.293    -.4139984    1.356546
        180  |   1.471274   1.274648     1.15   0.251    -1.061036    4.003584
        181  |  -.7226131   .4313674    -1.68   0.097      -1.5796    .1343735
        182  |  -.2129711   .3587092    -0.59   0.554    -.9256096    .4996674
        183  |   3.607543   .3352081    10.76   0.000     2.941593    4.273492
        184  |   .9070283   1.579562     0.57   0.567    -2.231048    4.045104
        185  |  -.7957286   .3930247    -2.02   0.046    -1.576541   -.0149165
        186  |  -.1837812   .4248366    -0.43   0.666    -1.027793    .6602308
        187  |  -.3924572   .3352081    -1.17   0.245    -1.058407    .2734921
        189  |  -.3924572   .3352081    -1.17   0.245    -1.058407    .2734921
        191  |  -.3924572   .3352081    -1.17   0.245    -1.058407    .2734921
        193  |  -.3924572   .3352081    -1.17   0.245    -1.058407    .2734921
        195  |  -.5209774   .3608809    -1.44   0.152     -1.23793    .1959754
        196  |   2.277048   2.235634     1.02   0.311    -2.164429    6.718524
        197  |  -.1638649   .5189425    -0.32   0.753    -1.194835     .867105
        198  |  -.4657963   .3461159    -1.35   0.182    -1.153416    .2218233
        199  |  -.4971982   .3180306    -1.56   0.121    -1.129021     .134625
        200  |  -.2761774   .3398614    -0.81   0.419    -.9513715    .3990167
        201  |   2.405907   .3728694     6.45   0.000     1.665137    3.146677
        202  |   4.880292   .2861066    17.06   0.000     4.311892    5.448693
        203  |   4.607543   .3352081    13.75   0.000     3.941593    5.273492
        204  |  -.2713509   .3573643    -0.76   0.450    -.9813175    .4386156
        205  |   -.226848   .3430727    -0.66   0.510    -.9084218    .4547257
        206  |   1.816047   1.738427     1.04   0.299    -1.637641    5.269735
        207  |   .9123095   1.395716     0.65   0.515    -1.860523    3.685142
        210  |  -.2701289   .3531061    -0.77   0.446    -.9716358    .4313779
        211  |  -.5008547   .3677665    -1.36   0.177    -1.231487    .2297776
        212  |  -.3895878    .335429    -1.16   0.249    -1.055976    .2768005
        213  |   -.363765   .4656685    -0.78   0.437    -1.288897    .5613668
        214  |  -.5765764   .4856314    -1.19   0.238    -1.541368    .3882153
        215  |   .6104122    .335429     1.82   0.072    -.0559761      1.2768
        216  |   1.269512   1.237813     1.03   0.308    -1.189619    3.728643
        217  |   1.481846   1.804029     0.82   0.414    -2.102172    5.065864
        219  |   1.440542   1.541195     0.93   0.352    -1.621311    4.502394
        221  |   .3816091   .4409473     0.87   0.389    -.4944097    1.257628
        227  |  -.4114752   .4512801    -0.91   0.364    -1.308022    .4850715
        231  |  -.3001836   .4050711    -0.74   0.461    -1.104928     .504561
        233  |  -.1588694   .3539463    -0.45   0.655    -.8620456    .5443068
        235  |  -.3618312   .3588392    -1.01   0.316    -1.074728    .3510655
        237  |   1.814213    .926034     1.96   0.053    -.0255155    3.653941
        239  |   5.800496   1.165346     4.98   0.000     3.485332    8.115659
        241  |  -.1624693   .3015308    -0.54   0.591    -.7615129    .4365744
        243  |  -.8525952   .4256687    -2.00   0.048     -1.69826   -.0069301
        244  |   -.297423   .3073429    -0.97   0.336    -.9080133    .3131672
        245  |  -.1624693   .3015308    -0.54   0.591    -.7615129    .4365744
        247  |   2.123392   1.256923     1.69   0.095    -.3737049    4.620488
        248  |  -.3645896   .3005503    -1.21   0.228    -.9616852     .232506
        250  |   -.470666   .3682713    -1.28   0.205    -1.202301    .2609693
        251  |  -.3963865   .3740653    -1.06   0.292    -1.139533    .3467595
        252  |  -.2915694   .2944602    -0.99   0.325     -.876566    .2934271
        267  |  -.3910852   .3271475    -1.20   0.235    -1.041021    .2588505
        269  |  -.1624693   .3015308    -0.54   0.591    -.7615129    .4365744
        271  |  -.8990372   .5234743    -1.72   0.089     -1.93901    .1409358
        272  |   -.503325   .3432388    -1.47   0.146    -1.185229    .1785789
        274  |   -.470666   .3682713    -1.28   0.205    -1.202301    .2609693
        275  |  -.5742363   .5120628    -1.12   0.265    -1.591538    .4430658
        276  |   -.297423   .3073429    -0.97   0.336    -.9080133    .3131672
        278  |   -.470666   .3682713    -1.28   0.205    -1.202301    .2609693
        279  |  -.5279694   .4054944    -1.30   0.196    -1.333555    .2776161
        280  |  -.2915694   .2944602    -0.99   0.325     -.876566    .2934271
        283  |  -.1818807     .35486    -0.51   0.610     -.886872    .5231106
        284  |   .3623419   .3376444     1.07   0.286    -.3084476    1.033131
        285  |  -.5085579   .3512719    -1.45   0.151    -1.206421     .189305
        287  |  -.3910852   .3271475    -1.20   0.235    -1.041021    .2588505
             |
      fYes_T |   .3460887    .178231     1.94   0.055    -.0079984    .7001757
        mage |  -.0120104   .0106238    -1.13   0.261    -.0331164    .0090955
    mmarried |    .130239   .2547041     0.51   0.610     -.375775    .6362531
       makan |  -.1910872   .2095356    -0.91   0.364    -.6073663    .2251918
mselfemplo~d |  -.3140977   .1875369    -1.67   0.097    -.6866725    .0584771
       m2q1a |   .0465443   .0458604     1.01   0.313    -.0445653    .1376539
      2.m3q1 |  -.0907505    .233176    -0.39   0.698    -.5539954    .3724944
        trt2 |  -.4390063   .2766246    -1.59   0.116    -.9885694    .1105567
        trt3 |  -.5748369   .2754472    -2.09   0.040    -1.122061    -.027613
        trt4 |  -.5545987   .2799325    -1.98   0.051    -1.110734    .0015362
       _cons |   1.104814   .4470624     2.47   0.015     .2166469    1.992982
------------------------------------------------------------------------------

. test _b[trt2]=_b[trt4]

 ( 1)  trt2 - trt4 = 0

       F(  1,    90) =    0.35
            Prob > F =    0.5539

. test _b[trt3]=_b[trt4]

 ( 1)  trt3 - trt4 = 0

       F(  1,    90) =    0.01
            Prob > F =    0.9237

. test _b[trt2]=_b[trt3]

 ( 1)  trt2 - trt3 = 0

       F(  1,    90) =    0.68
            Prob > F =    0.4115

. test _b[trt2] + _b[trt3] =_b[trt4]

 ( 1)  trt2 + trt3 - trt4 = 0

       F(  1,    90) =    1.63
            Prob > F =    0.2046

. 
. reg ihs_fdamt i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q
> 1a i.m3q1 trt, r cluster(uniqueVendorID) level(95) //only rep vendors

Linear regression                               Number of obs     =        335
                                                F(74, 90)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6636
                                                Root MSE          =     .51048

                        (Std. err. adjusted for 91 clusters in uniqueVendorID)
------------------------------------------------------------------------------
             |               Robust
   ihs_fdamt | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.2772324   .1789643    -1.55   0.125    -.6327762    .0783114
          5  |   .9594818   .8043675     1.19   0.236    -.6385347    2.557498
          6  |  -.0975332   .2517975    -0.39   0.699    -.5977729    .4027065
          7  |  -.2772324   .1789643    -1.55   0.125    -.6327762    .0783114
          8  |     -.0332   .1228652    -0.27   0.788    -.2772932    .2108932
         11  |  -.0295937   .1524106    -0.19   0.846     -.332384    .2731965
         14  |  -.0295937   .1524106    -0.19   0.846     -.332384    .2731965
         18  |  -.0975332   .2517975    -0.39   0.699    -.5977729    .4027065
         22  |  -.2772324   .1789643    -1.55   0.125    -.6327762    .0783114
         23  |  -.0414445   .1204858    -0.34   0.732    -.2808107    .1979216
         24  |  -.0975332   .2517975    -0.39   0.699    -.5977729    .4027065
         26  |  -.1688184   .2057276    -0.82   0.414    -.5775321    .2398954
         27  |  -.0975332   .2517975    -0.39   0.699    -.5977729    .4027065
         29  |  -.1683103   .2102759    -0.80   0.426    -.5860601    .2494394
         30  |  -.0975332   .2517975    -0.39   0.699    -.5977729    .4027065
         32  |   .0213526   .2195175     0.10   0.923    -.4147572    .4574624
         33  |  -.0975332   .2517975    -0.39   0.699    -.5977729    .4027065
         34  |  -.2772324   .1789643    -1.55   0.125    -.6327762    .0783114
         35  |  -.0732426   .1158072    -0.63   0.529    -.3033138    .1568286
         37  |  -.1498706   .1377405    -1.09   0.279    -.4235161    .1237749
         38  |    .067049   .3357571     0.20   0.842    -.5999912    .7340891
         39  |  -.1310828   .1329339    -0.99   0.327    -.3951793    .1330136
         40  |   .4310805   .4568631     0.94   0.348    -.4765579    1.338719
         41  |    -.05356   .2070831    -0.26   0.797    -.4649667    .3578467
         42  |    .088055   .2524573     0.35   0.728    -.4134955    .5896055
         51  |  -.1755912   .1283842    -1.37   0.175    -.4306489    .0794664
         52  |  -.3877985   .2007085    -1.93   0.056    -.7865409    .0109439
         53  |  -.1516921   .1364297    -1.11   0.269    -.4227336    .1193493
         54  |  -.1029562   .2479053    -0.42   0.679    -.5954633    .3895508
         55  |  -.1256173   .1641257    -0.77   0.446    -.4516817    .2004472
         56  |  -.0439675   .1961486    -0.22   0.823    -.4336509     .345716
         57  |   1.024691   .5018964     2.04   0.044     .0275866    2.021796
         58  |   .5672478      .2743     2.07   0.042     .0223029    1.112193
         61  |  -.1567834   .1749103    -0.90   0.372    -.5042733    .1907064
         62  |  -.2303827   .1382801    -1.67   0.099    -.5051002    .0443348
         63  |  -.2275135   .2043446    -1.11   0.269    -.6334798    .1784528
         64  |  -.4510066   .1970062    -2.29   0.024    -.8423938   -.0596195
         65  |  -.0051549   .2467558    -0.02   0.983    -.4953784    .4850687
         66  |    1.83222   .2026847     9.04   0.000     1.429551    2.234889
         67  |   1.021076   .5129706     1.99   0.050       .00197    2.040181
         68  |  -.4510066   .1970062    -2.29   0.024    -.8423938   -.0596195
         69  |   .0650917   .1866032     0.35   0.728    -.3056282    .4358116
         71  |   .2016028   .2109249     0.96   0.342    -.2174363    .6206419
         72  |  -.4510066   .1970062    -2.29   0.024    -.8423938   -.0596195
         89  |   .0335475    .180557     0.19   0.853    -.3251605    .3922555
         90  |   -.209211   .1755365    -1.19   0.236    -.5579448    .1395228
         91  |  -.3163342   .2746248    -1.15   0.252    -.8619244    .2292559
         93  |   .0661612   .1587419     0.42   0.678    -.2492073    .3815296
         94  |  -.3509756   .2204504    -1.59   0.115    -.7889388    .0869875
         95  |  -.3389449   .2250129    -1.51   0.135    -.7859723    .1080826
         97  |  -.0819781    .142297    -0.58   0.566     -.364676    .2007198
         98  |  -.2303827   .1382801    -1.67   0.099    -.5051002    .0443348
         99  |  -.2746431   .2490078    -1.10   0.273    -.7693406    .2200545
        102  |   .2455057   .5691015     0.43   0.667    -.8851137    1.376125
        103  |   .9689475   .1249117     7.76   0.000     .7207885    1.217106
        104  |   .2534007   .2343714     1.08   0.282    -.2122189    .7190203
        107  |  -.0127851   .1489247    -0.09   0.932      -.30865    .2830798
        109  |  -.2889176   .1848518    -1.56   0.122    -.6561581    .0783228
        110  |  -.1415456   .1603736    -0.88   0.380    -.4601558    .1770645
        113  |     -.1706   .1402297    -1.22   0.227    -.4491908    .1079907
        114  |  -.2807784   .3080917    -0.91   0.365    -.8928562    .3312995
        117  |     -.1706   .1402297    -1.22   0.227    -.4491908    .1079907
        118  |  -.3488662   .1717622    -2.03   0.045    -.6901018   -.0076307
        137  |  -.3475663   .1559826    -2.23   0.028    -.6574529   -.0376797
        138  |  -.2571233   .1660508    -1.55   0.125    -.5870122    .0727656
        141  |  -.4072352   .1714455    -2.38   0.020    -.7478416   -.0666288
        142  |  -.3080416   .1795466    -1.72   0.090    -.6647423    .0486591
        145  |   -.230269   .1443891    -1.59   0.114    -.5171232    .0565852
        146  |  -.2822108   .1597608    -1.77   0.081    -.5996036     .035182
        149  |   .5213832   .6901416     0.76   0.452    -.8497036     1.89247
        150  |   .6373213   .2326746     2.74   0.007     .1750725     1.09957
        153  |     -.1706   .1402297    -1.22   0.227    -.4491908    .1079907
        154  |  -.0131289   .1872462    -0.07   0.944     -.385126    .3588683
        157  |  -.1203361   .2008974    -0.60   0.551    -.5194538    .2787815
        158  |  -.0355273   .2528869    -0.14   0.889    -.5379313    .4668767
        159  |  -.0051782   .1259451    -0.04   0.967    -.2553901    .2450338
        160  |   1.895781    .216545     8.75   0.000     1.465576    2.325985
        162  |   .6824416    .216545     3.15   0.002     .2522372    1.112646
        171  |  -.2354941   .2664681    -0.88   0.379    -.7648796    .2938914
        172  |   .1278774   .2060083     0.62   0.536     -.281394    .5371488
        173  |  -.2354941   .2664681    -0.88   0.379    -.7648796    .2938914
        174  |   -.198932    .216545    -0.92   0.361    -.6291364    .2312725
        175  |  -.1203361   .2008974    -0.60   0.551    -.5194538    .2787815
        176  |   -.198932    .216545    -0.92   0.361    -.6291364    .2312725
        177  |   .6992292   .1480628     4.72   0.000     .4050766    .9933818
        178  |   .4051595   .3344053     1.21   0.229     -.259195    1.069514
        180  |    .873696   .7544068     1.16   0.250    -.6250649    2.372457
        181  |  -.4159395   .2217836    -1.88   0.064    -.8565513    .0246724
        182  |  -.1258373    .168557    -0.75   0.457    -.4607053    .2090307
        183  |     1.8369   .1706043    10.77   0.000     1.497965    2.175835
        184  |   .4797813   .8236327     0.58   0.562    -1.156509    2.116071
        185  |  -.4335982   .1843942    -2.35   0.021    -.7999294   -.0672669
        186  |  -.0959691   .2043072    -0.47   0.640     -.501861    .3099227
        187  |  -.2578125   .1706043    -1.51   0.134    -.5967478    .0811228
        189  |  -.2578125   .1706043    -1.51   0.134    -.5967478    .0811228
        191  |  -.2578125   .1706043    -1.51   0.134    -.5967478    .0811228
        193  |  -.2578125   .1706043    -1.51   0.134    -.5967478    .0811228
        195  |  -.3280466   .1813328    -1.81   0.074    -.6882959    .0322026
        196  |   1.020515   1.000959     1.02   0.311    -.9680642    3.009094
        197  |  -.0104505   .3942898    -0.03   0.979    -.7937761     .772875
        198  |   -.276508   .1534826    -1.80   0.075    -.5814279     .028412
        199  |  -.3042417   .1530731    -1.99   0.050    -.6083481   -.0001353
        200  |   -.163505   .1575052    -1.04   0.302    -.4764166    .1494067
        201  |   1.472741   .1709827     8.61   0.000     1.133054    1.812428
        202  |   2.212897   .1203016    18.39   0.000     1.973896    2.451897
        203  |   2.054626   .1706043    12.04   0.000      1.71569    2.393561
        204  |  -.1855736   .1640686    -1.13   0.261    -.5115246    .1403774
        205  |  -.1214312   .1594092    -0.76   0.448    -.4381255     .195263
        206  |    .945129   .9004875     1.05   0.297    -.8438467    2.734105
        207  |   .4124434   .6211893     0.66   0.508    -.8216575    1.646544
        210  |  -.1380986   .1615653    -0.85   0.395    -.4590763     .182879
        211  |  -.2560761   .1771206    -1.45   0.152    -.6079571    .0958048
        212  |  -.2219166   .1524661    -1.46   0.149    -.5248171    .0809839
        213  |  -.1762833   .2075057    -0.85   0.398    -.5885295     .235963
        214  |  -.2729551   .2215443    -1.23   0.221    -.7130916    .1671814
        215  |    .659457   .1524661     4.33   0.000     .3565565    .9623575
        216  |   .8132344   .7776483     1.05   0.298    -.7316997    2.358169
        217  |   .6682184   .8279422     0.81   0.422    -.9766333     2.31307
        219  |   .7593179   .7928367     0.96   0.341    -.8157906    2.334426
        221  |   .4437621   .3285162     1.35   0.180    -.2088927    1.096417
        227  |  -.2109243   .1959373    -1.08   0.285    -.6001879    .1783393
        231  |  -.1615831   .1886174    -0.86   0.394    -.5363045    .2131382
        233  |  -.0797882   .1707181    -0.47   0.641    -.4189496    .2593732
        235  |  -.1855833   .1715665    -1.08   0.282    -.5264301    .1552636
        237  |   1.259725    .440154     2.86   0.005     .3852823    2.134168
        239  |   2.390048   .2710592     8.82   0.000     1.851541    2.928554
        241  |  -.1120185   .1301035    -0.86   0.392    -.3704918    .1464548
        243  |  -.4942926   .2077521    -2.38   0.019    -.9070284   -.0815569
        244  |  -.1531332    .120043    -1.28   0.205    -.3916196    .0853532
        245  |  -.1120185   .1301035    -0.86   0.392    -.3704918    .1464548
        247  |   1.075417   .6380884     1.69   0.095    -.1922566    2.343092
        248  |  -.1524285   .1205828    -1.26   0.209    -.3919874    .0871303
        250  |  -.2676994   .1778796    -1.50   0.136    -.6210883    .0856895
        251  |  -.1230778   .2469241    -0.50   0.619    -.6136355    .3674799
        252  |  -.1203709   .1217544    -0.99   0.325    -.3622573    .1215154
        267  |  -.2233259    .144106    -1.55   0.125    -.5096177    .0629659
        269  |  -.1120185   .1301035    -0.86   0.392    -.3704918    .1464548
        271  |  -.5136405   .2698264    -1.90   0.060    -1.049698    .0224168
        272  |  -.2987465   .1510613    -1.98   0.051    -.5988561    .0013631
        274  |  -.2676994   .1778796    -1.50   0.136    -.6210883    .0856895
        275  |  -.3339561   .2623301    -1.27   0.206    -.8551206    .1872084
        276  |  -.1531332    .120043    -1.28   0.205    -.3916196    .0853532
        278  |  -.2676994   .1778796    -1.50   0.136    -.6210883    .0856895
        279  |  -.2898089   .1939021    -1.49   0.139    -.6750293    .0954114
        280  |  -.1203709   .1217544    -0.99   0.325    -.3622573    .1215154
        283  |  -.1128427   .1684028    -0.67   0.505    -.4474042    .2217188
        284  |   .5840364   .1566088     3.73   0.000     .2729056    .8951672
        285  |  -.2889848   .1656386    -1.74   0.084    -.6180547    .0400851
        287  |  -.2233259    .144106    -1.55   0.125    -.5096177    .0629659
             |
      fYes_T |   .1769663   .0888941     1.99   0.050     .0003627    .3535698
        mage |  -.0069431   .0056746    -1.22   0.224    -.0182166    .0043304
    mmarried |   .0538371   .1261322     0.43   0.671    -.1967465    .3044208
       makan |  -.0890688   .1091163    -0.82   0.416    -.3058474    .1277098
mselfemplo~d |  -.1723649   .0962772    -1.79   0.077    -.3636365    .0189066
       m2q1a |   .0263629   .0264459     1.00   0.322    -.0261764    .0789023
      2.m3q1 |  -.0519374   .1209513    -0.43   0.669    -.2922284    .1883535
         trt |  -.3236926   .1384358    -2.34   0.022    -.5987194   -.0486658
       _cons |   .6400971   .2210431     2.90   0.005     .2009564    1.079238
------------------------------------------------------------------------------

. nlcom _b[trt]*xbar*((sqrt(ybar^2+1))/ybar)

       _nl_1: _b[trt]*xbar*((sqrt(ybar^2+1))/ybar)

------------------------------------------------------------------------------
   ihs_fdamt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       _nl_1 |  -.6318346   .2702208    -2.34   0.019    -1.161458   -.1022115
------------------------------------------------------------------------------

. **interpret, ihs: pos and signif => the implied elasticity=0.752
. reg ihs_fdamt i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q
> 1a i.m3q1 trt2 trt3 trt4, r cluster(uniqueVendorID) level(95) //only rep ven
> dors

Linear regression                               Number of obs     =        335
                                                F(76, 90)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6650
                                                Root MSE          =     .51234

                        (Std. err. adjusted for 91 clusters in uniqueVendorID)
------------------------------------------------------------------------------
             |               Robust
   ihs_fdamt | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.2564068   .1952967    -1.31   0.193    -.6443978    .1315842
          5  |   .9801978   .8172561     1.20   0.234    -.6434241     2.60382
          6  |  -.1505601    .266064    -0.57   0.573    -.6791427    .3780226
          7  |  -.2564068   .1952967    -1.31   0.193    -.6443978    .1315842
          8  |   .0018181   .1431441     0.01   0.990    -.2825627     .286199
         11  |   .0175204   .1728071     0.10   0.919     -.325791    .3608318
         14  |   .0175204   .1728071     0.10   0.919     -.325791    .3608318
         18  |  -.1505601    .266064    -0.57   0.573    -.6791427    .3780226
         22  |  -.2564068   .1952967    -1.31   0.193    -.6443978    .1315842
         23  |  -.0144079   .1547576    -0.09   0.926     -.321861    .2930452
         24  |  -.1505601    .266064    -0.57   0.573    -.6791427    .3780226
         26  |  -.1357225    .226643    -0.60   0.551    -.5859884    .3145434
         27  |  -.1505601    .266064    -0.57   0.573    -.6791427    .3780226
         29  |  -.1413738   .2263696    -0.62   0.534    -.5910965     .308349
         30  |  -.1505601    .266064    -0.57   0.573    -.6791427    .3780226
         32  |   .0551518   .2380956     0.23   0.817    -.4178667    .5281704
         33  |  -.1505601    .266064    -0.57   0.573    -.6791427    .3780226
         34  |  -.2564068   .1952967    -1.31   0.193    -.6443978    .1315842
         35  |  -.0627611   .1316199    -0.48   0.635    -.3242471    .1987249
         37  |  -.1559539   .1599472    -0.98   0.332     -.473717    .1618092
         38  |   .0801441   .3498654     0.23   0.819    -.6149245    .7752126
         39  |  -.1563142   .1580139    -0.99   0.325    -.4702363     .157608
         40  |   .4583602   .4653505     0.98   0.327    -.4661398     1.38286
         41  |  -.0566842   .2273376    -0.25   0.804    -.5083301    .3949616
         42  |   .1163507   .2581324     0.45   0.653    -.3964745    .6291758
         51  |    -.17421   .1535896    -1.13   0.260    -.4793425    .1309225
         52  |  -.3578023    .234227    -1.53   0.130    -.8231351    .1075305
         53  |   -.183867    .168133    -1.09   0.277    -.5178926    .1501586
         54  |  -.0692402   .2580723    -0.27   0.789    -.5819459    .4434654
         55  |  -.1402076   .1745188    -0.80   0.424    -.4869196    .2065045
         56  |  -.0115272   .2110952    -0.05   0.957    -.4309047    .4078502
         57  |    1.01254    .481999     2.10   0.038      .054965    1.970115
         58  |   .5954099   .3017358     1.97   0.052     -.004041    1.194861
         61  |  -.1238538   .1946903    -0.64   0.526      -.51064    .2629325
         62  |   -.198671   .1605422    -1.24   0.219     -.517616    .1202741
         63  |  -.2229294    .224971    -0.99   0.324    -.6698736    .2240147
         64  |  -.4257171   .2100702    -2.03   0.046    -.8430582   -.0083759
         65  |   .0181176    .258708     0.07   0.944    -.4958509    .5320861
         66  |   1.850705   .2152256     8.60   0.000     1.423122    2.278288
         67  |   1.046888   .5171462     2.02   0.046     .0194866    2.074289
         68  |  -.4257171   .2100702    -2.03   0.046    -.8430582   -.0083759
         69  |   .1103154   .2092375     0.53   0.599    -.3053714    .5260023
         71  |   .1957817   .2354775     0.83   0.408    -.2720354    .6635988
         72  |  -.4257171   .2100702    -2.03   0.046    -.8430582   -.0083759
         89  |   .0653957   .2038505     0.32   0.749    -.3395889    .4703804
         90  |  -.1908848   .1908745    -1.00   0.320    -.5700903    .1883206
         91  |   -.317212   .2766555    -1.15   0.255    -.8668364    .2324124
         93  |    .099499    .176663     0.56   0.575     -.251473    .4504709
         94  |  -.3305771   .2311826    -1.43   0.156    -.7898617    .1287076
         95  |  -.3120638   .2428714    -1.28   0.202    -.7945703    .1704427
         97  |  -.0509256   .1665945    -0.31   0.761    -.3818948    .2800435
         98  |   -.198671   .1605422    -1.24   0.219     -.517616    .1202741
         99  |  -.2470945   .2637471    -0.94   0.351    -.7710742    .2768853
        102  |   .2754626   .5897643     0.47   0.642    -.8962071    1.447132
        103  |   .9543284   .1609901     5.93   0.000     .6344934    1.274163
        104  |   .2825175   .2454623     1.15   0.253    -.2051361    .7701711
        107  |  -.0107994   .1841386    -0.06   0.953    -.3766227     .355024
        109  |  -.3063068   .2289956    -1.34   0.184    -.7612466     .148633
        110  |  -.1360146    .179676    -0.76   0.451    -.4929723    .2209431
        113  |  -.1525082    .172953    -0.88   0.380    -.4961094     .191093
        114  |  -.3047896   .2930159    -1.04   0.301    -.8869167    .2773375
        117  |  -.1525082    .172953    -0.88   0.380    -.4961094     .191093
        118  |  -.3271514   .1934281    -1.69   0.094    -.7114302    .0571274
        137  |  -.3256472   .1844953    -1.77   0.081    -.6921793    .0408849
        138  |  -.2617407   .1896946    -1.38   0.171     -.638602    .1151207
        141  |  -.4601055   .2014686    -2.28   0.025     -.860358    -.059853
        142  |   -.296401   .1988611    -1.49   0.140    -.6914732    .0986712
        145  |  -.2869665   .1834295    -1.56   0.121    -.6513813    .0774483
        146  |  -.2591942   .1819226    -1.42   0.158    -.6206152    .1022269
        149  |   .5020804   .6650608     0.75   0.452    -.8191789     1.82334
        150  |   .6273444   .2361214     2.66   0.009      .158248    1.096441
        153  |  -.1525082    .172953    -0.88   0.380    -.4961094     .191093
        154  |  -.0795188   .2240036    -0.35   0.723     -.524541    .3655034
        157  |  -.1107266   .2183664    -0.51   0.613    -.5445496    .3230965
        158  |  -.0110625   .2649507    -0.04   0.967    -.5374332    .5153082
        159  |   .0089598   .1633512     0.05   0.956    -.3155659    .3334854
        160  |   1.921319   .2303782     8.34   0.000     1.463632    2.379005
        162  |   .7079798   .2303782     3.07   0.003     .2502933    1.165666
        171  |  -.2304129   .2770694    -0.83   0.408    -.7808596    .3200339
        172  |   .1512688   .2188729     0.69   0.491    -.2835604     .586098
        173  |  -.2304129   .2770694    -0.83   0.408    -.7808596    .3200339
        174  |  -.1733938   .2303782    -0.75   0.454    -.6310802    .2842927
        175  |  -.1107266   .2183664    -0.51   0.613    -.5445496    .3230965
        176  |  -.1733938   .2303782    -0.75   0.454    -.6310802    .2842927
        177  |   .7171943   .1792433     4.00   0.000     .3610962    1.073292
        178  |   .4296243   .3457368     1.24   0.217    -.2572423    1.116491
        180  |   .8981608   .7635664     1.18   0.243    -.6187972    2.415119
        181  |  -.3917694   .2354279    -1.66   0.100     -.859488    .0759492
        182  |  -.0938236   .1910983    -0.49   0.625    -.4734737    .2858265
        183  |   1.857141   .1880585     9.88   0.000      1.48353    2.230752
        184  |   .4888672   .8398118     0.58   0.562    -1.179566      2.1573
        185  |  -.4088319   .2074166    -1.97   0.052    -.8209013    .0032374
        186  |  -.0703179   .2217074    -0.32   0.752    -.5107784    .3701426
        187  |  -.2375712   .1880585    -1.26   0.210    -.6111823    .1360399
        189  |  -.2375712   .1880585    -1.26   0.210    -.6111823    .1360399
        191  |  -.2375712   .1880585    -1.26   0.210    -.6111823    .1360399
        193  |  -.2375712   .1880585    -1.26   0.210    -.6111823    .1360399
        195  |   -.306139   .1965994    -1.56   0.123    -.6967181      .08444
        196  |   1.053771    1.02213     1.03   0.305    -.9768683     3.08441
        197  |   .0111342   .4034996     0.03   0.978    -.7904881    .8127565
        198  |  -.2307893   .1843135    -1.25   0.214    -.5969602    .1353815
        199  |   -.282657    .173935    -1.63   0.108    -.6282093    .0628953
        200  |   -.128065    .182812    -0.70   0.485     -.491253     .235123
        201  |   1.495245   .1914286     7.81   0.000     1.114939    1.875551
        202  |   2.254788   .1533336    14.71   0.000     1.950164    2.559412
        203  |   2.074867   .1880585    11.03   0.000     1.701256    2.448478
        204  |   -.140835   .1912261    -0.74   0.463     -.520739    .2390689
        205  |    -.11519   .1884515    -0.61   0.543    -.4895817    .2592017
        206  |    .967335   .9026167     1.07   0.287    -.8258707    2.760541
        207  |   .4211473   .6398952     0.66   0.512    -.8501162    1.692411
        210  |  -.1410476   .1951986    -0.72   0.472    -.5288438    .2467485
        211  |  -.2564736   .1963701    -1.31   0.195    -.6465971    .1336498
        212  |  -.2023455   .1804769    -1.12   0.265    -.5608944    .1562033
        213  |  -.1749241   .2532131    -0.69   0.491    -.6779762     .328128
        214  |  -.2854665   .2687776    -1.06   0.291    -.8194401    .2485071
        215  |    .679028   .1804769     3.76   0.000     .3204792    1.037577
        216  |   .7968957   .7499532     1.06   0.291    -.6930172    2.286809
        217  |   .6982033   .8427043     0.83   0.410    -.9759758    2.372382
        219  |   .7681745   .8007556     0.96   0.340    -.8226663    2.359015
        221  |   .4358363   .3231465     1.35   0.181    -.2061505    1.077823
        227  |   -.179868   .2357729    -0.76   0.448    -.6482719     .288536
        231  |  -.1303224    .212161    -0.61   0.541    -.5518173    .2911725
        233  |  -.0768013   .1952908    -0.39   0.695    -.4647806    .3111779
        235  |  -.1738101   .1958137    -0.89   0.377    -.5628281    .2152079
        237  |   1.253647   .4189516     2.99   0.004     .4213267    2.085968
        239  |   2.378693   .3163546     7.52   0.000       1.7502    3.007187
        241  |  -.0858361   .1615729    -0.53   0.597    -.4068289    .2351567
        243  |  -.4778774   .2237288    -2.14   0.035    -.9223537   -.0334011
        244  |  -.1607287   .1700176    -0.95   0.347    -.4984984     .177041
        245  |  -.0858361   .1615729    -0.53   0.597    -.4068289    .2351567
        247  |   1.095891   .6415402     1.71   0.091    -.1786405    2.370423
        248  |  -.2065414   .1645458    -1.26   0.213    -.5334403    .1203575
        250  |  -.2466862   .1995882    -1.24   0.220     -.643203    .1498306
        251  |  -.1028593   .2617386    -0.39   0.695    -.6228487    .4171301
        252  |   -.170457   .1600178    -1.07   0.290    -.4883603    .1474463
        267  |  -.2035184   .1753544    -1.16   0.249    -.5518905    .1448537
        269  |  -.0858361   .1615729    -0.53   0.597    -.4068289    .2351567
        271  |  -.4932165   .2820629    -1.75   0.084    -1.053584    .0671507
        272  |  -.2519706    .183227    -1.38   0.172    -.6159829    .1120418
        274  |  -.2466862   .1995882    -1.24   0.220     -.643203    .1498306
        275  |    -.31081   .2800493    -1.11   0.270    -.8671768    .2455568
        276  |  -.1607287   .1700176    -0.95   0.347    -.4984984     .177041
        278  |  -.2466862   .1995882    -1.24   0.220     -.643203    .1498306
        279  |  -.2612356   .2173689    -1.20   0.233    -.6930768    .1706055
        280  |   -.170457   .1600178    -1.07   0.290    -.4883603    .1474463
        283  |  -.0880967   .1943064    -0.45   0.651    -.4741203    .2979269
        284  |   .5377776   .1836657     2.93   0.004     .1728935    .9026616
        285  |  -.2589751   .1888838    -1.37   0.174    -.6342258    .1162756
        287  |  -.2035184   .1753544    -1.16   0.249    -.5518905    .1448537
             |
      fYes_T |    .173139   .0897987     1.93   0.057    -.0052617    .3515397
        mage |  -.0066695   .0056833    -1.17   0.244    -.0179604    .0046214
    mmarried |   .0552353   .1320831     0.42   0.677    -.2071708    .3176414
       makan |   -.096501   .1106954    -0.87   0.386    -.3164168    .1234148
mselfemplo~d |  -.1681374   .0952985    -1.76   0.081    -.3574646    .0211897
       m2q1a |   .0255051   .0254976     1.00   0.320    -.0251504    .0761605
      2.m3q1 |  -.0575079   .1251025    -0.46   0.647    -.3060458    .1910299
        trt2 |  -.2485905   .1489922    -1.67   0.099    -.5445896    .0474085
        trt3 |  -.3413888   .1489771    -2.29   0.024    -.6373579   -.0454198
        trt4 |  -.3253692   .1486821    -2.19   0.031    -.6207522   -.0299861
       _cons |   .6179364   .2349796     2.63   0.010     .1511084    1.084764
------------------------------------------------------------------------------

. test _b[trt2]=_b[trt4]

 ( 1)  trt2 - trt4 = 0

       F(  1,    90) =    0.57
            Prob > F =    0.4516

. test _b[trt3]=_b[trt4]

 ( 1)  trt3 - trt4 = 0

       F(  1,    90) =    0.02
            Prob > F =    0.8805

. test _b[trt2]=_b[trt3]

 ( 1)  trt2 - trt3 = 0

       F(  1,    90) =    1.04
            Prob > F =    0.3112

. test _b[trt2] + _b[trt3] =_b[trt4]

 ( 1)  trt2 + trt3 - trt4 = 0

       F(  1,    90) =    1.97
            Prob > F =    0.1642

. 
. 
. 
. ** Table 7 -----------------------------------------------------------------
> ----
. *SPILLOVERS - untreated vendors: note: no baseline X-s here (we only tracked
>  them at endline)
. gen uniqueLocalityID=ge02

. sum fd fdamt ihs_fdamt if trt==0 & _merge==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
          fd |         97    .2783505    .4505152          0          1
       fdamt |         97    .7835052    1.522243          0          5
   ihs_fdamt |         97    .4459032    .7909557          0   2.312438

. /*
> *to get the same n=411, adjust the ff (or just leave n=405, yield similar re
> sults):
> replace trt2=1 if missing(trt2) & trt==1
> replace trt3=1 if missing(trt3) & trt==1
> replace trt4=1 if missing(trt4) & trt==1
> */
. reg fd i.distXtrXdateFes trt if _merge==1, r cluster(uniqueLocalityID) level
> (95) //spillover (non-rep vendors)

Linear regression                               Number of obs     =        411
                                                F(24, 62)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.7148
                                                Root MSE          =     .25394

                      (Std. err. adjusted for 63 clusters in uniqueLocalityID)
------------------------------------------------------------------------------
             |               Robust
          fd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          2  |   .1698041   .0675586     2.51   0.015     .0347565    .3048517
          3  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
          4  |  -5.83e-15   1.18e-07    -0.00   1.000    -2.36e-07    2.36e-07
          5  |   .2809152   .1448734     1.94   0.057    -.0086826     .570513
          6  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
          8  |   .2809152   .1448734     1.94   0.057    -.0086826     .570513
          9  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         14  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         17  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         18  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         20  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         21  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         22  |  -5.82e-15   1.18e-07    -0.00   1.000    -2.36e-07    2.36e-07
         23  |   .1698041   .0675586     2.51   0.015     .0347565    .3048517
         24  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         25  |  -5.80e-15   1.00e-07    -0.00   1.000    -2.01e-07    2.01e-07
         26  |   .1698041   .0675586     2.51   0.015     .0347565    .3048517
         27  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         28  |  -5.83e-15   1.00e-07    -0.00   1.000    -2.01e-07    2.01e-07
         29  |   .1698041   .0675586     2.51   0.015     .0347565    .3048517
         30  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         32  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         35  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         36  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         37  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         38  |   .4137397   .2476336     1.67   0.100    -.0812729    .9087522
         39  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         40  |   .4137397   .2476336     1.67   0.100    -.0812729    .9087522
         41  |   .5516529   .3629716     1.52   0.134     -.173917    1.277223
         42  |   .4137397   .2476336     1.67   0.100    -.0812729    .9087522
         48  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
         50  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
         51  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         52  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         53  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         54  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         55  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         56  |   .1637397   .0594339     2.75   0.008      .044933    .2825463
         57  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
         58  |    1.10916   .0940016    11.80   0.000     .9212533    1.297066
         61  |   .7183196   .4684223     1.53   0.130    -.2180433    1.654682
         62  |   .1455464   .0946645     1.54   0.129    -.0436852    .3347779
         63  |   .1091598   .0773485     1.41   0.163    -.0454576    .2637772
         64  |  -5.86e-15   1.00e-07    -0.00   1.000    -2.01e-07    2.01e-07
         65  |   .7183196   .4684223     1.53   0.130    -.2180433    1.654682
         66  |   .1455464   .0946645     1.54   0.129    -.0436852    .3347779
         67  |   .6091598    .259856     2.34   0.022     .0897151    1.128604
         68  |  -5.82e-15   1.00e-07    -0.00   1.000    -2.01e-07    2.01e-07
         69  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
         71  |   .6091598    .259856     2.34   0.022     .0897151    1.128604
         72  |  -5.83e-15   1.00e-07    -0.00   1.000    -2.01e-07    2.01e-07
         74  |          1   1.00e-07  1.0e+07   0.000     .9999998           1
         82  |  -5.84e-15   1.00e-07    -0.00   1.000    -2.01e-07    2.01e-07
         89  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         90  |   .1455464   .0946645     1.54   0.129    -.0436852    .3347779
         91  |   .1091598   .0773485     1.41   0.163    -.0454576    .2637772
         92  |  -5.81e-15   1.00e-07    -0.00   1.000    -2.01e-07    2.01e-07
         93  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         94  |   .1455464   .0946645     1.54   0.129    -.0436852    .3347779
         95  |   .1091598   .0773485     1.41   0.163    -.0454576    .2637772
         96  |  -5.80e-15   1.00e-07    -0.00   1.000    -2.01e-07    2.01e-07
         97  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         98  |   .1455464   .0946645     1.54   0.129    -.0436852    .3347779
         99  |   .1091598   .0773485     1.41   0.163    -.0454576    .2637772
        100  |  -5.84e-15   1.00e-07    -0.00   1.000    -2.01e-07    2.01e-07
        101  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
        102  |          1   1.00e-07  1.0e+07   0.000     .9999998           1
        103  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
        104  |          1   1.00e-07  1.0e+07   0.000     .9999998           1
        105  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
        107  |   .4788797   .2870073     1.67   0.100    -.0948397    1.052599
        108  |  -5.80e-15   1.00e-07    -0.00   1.000    -2.01e-07    2.01e-07
        109  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        110  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        111  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        112  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        113  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        114  |   .8849862   .3629716     2.44   0.018     .1594163    1.610556
        115  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        116  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        117  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        118  |   .8849862   .3629716     2.44   0.018     .1594163    1.610556
        119  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        120  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        137  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        138  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        139  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        140  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        141  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        142  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        143  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        144  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        145  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        146  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        147  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        148  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        149  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
        150  |   .7183196   .4684223     1.53   0.130    -.2180433    1.654682
        151  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
        152  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
        154  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        157  |  -5.85e-15   1.00e-07    -0.00   1.000    -2.01e-07    2.01e-07
        158  |   .1455464    .089287     1.63   0.108    -.0329357    .3240285
        159  |  -5.84e-15   1.01e-07    -0.00   1.000    -2.01e-07    2.01e-07
        160  |   .4788797   .2823863     1.70   0.095    -.0856026    1.043362
        161  |  -5.83e-15   1.01e-07    -0.00   1.000    -2.03e-07    2.03e-07
        162  |   .1455464    .089287     1.63   0.108    -.0329357    .3240285
        171  |   .1091598   .1063651     1.03   0.309     -.103461    .3217805
        172  |   .1455464    .089287     1.63   0.108    -.0329357    .3240285
        173  |  -5.84e-15   1.01e-07    -0.00   1.000    -2.02e-07    2.02e-07
        174  |   .1455464    .089287     1.63   0.108    -.0329357    .3240285
        175  |   .1091598   .1063651     1.03   0.309     -.103461    .3217805
        176  |   .1455464    .089287     1.63   0.108    -.0329357    .3240285
        177  |          1   1.01e-07  9.9e+06   0.000     .9999998           1
        178  |   .6091598   .3628815     1.68   0.098    -.1162299    1.334549
        179  |          1   7.82e-08  1.3e+07   0.000     .9999998           1
        182  |   .5516529   .3629716     1.52   0.134     -.173917    1.277223
        184  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        186  |   .5516529   .3629716     1.52   0.134     -.173917    1.277223
        196  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        198  |   .5516529   .3629716     1.52   0.134     -.173917    1.277223
        200  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        202  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
        205  |   .1637397   .0840645     1.95   0.056    -.0043028    .3317821
        206  |   .9137397   .2634322     3.47   0.001     .3871462    1.440333
        207  |   .9137397   .2634322     3.47   0.001     .3871462    1.440333
        212  |   .1637397   .0840645     1.95   0.056    -.0043028    .3317821
        213  |   .1637397   .0840645     1.95   0.056    -.0043028    .3317821
        214  |   .1637397   .0840645     1.95   0.056    -.0043028    .3317821
        215  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
        217  |   .1455464   .0922019     1.58   0.120    -.0387626    .3298553
        218  |  -5.82e-15   7.87e-08    -0.00   1.000    -1.57e-07    1.57e-07
        219  |    .812213   .4406785     1.84   0.070    -.0686908    1.693117
        220  |          1   7.87e-08  1.3e+07   0.000     .9999998           1
        221  |    .812213   .3221391     2.52   0.014     .1682661     1.45616
        222  |          1   7.85e-08  1.3e+07   0.000     .9999998           1
        227  |  -5.89e-15   7.87e-08    -0.00   1.000    -1.57e-07    1.57e-07
        231  |   .1455464   .0922019     1.58   0.120    -.0387626    .3298553
        232  |  -5.80e-15   7.87e-08    -0.00   1.000    -1.57e-07    1.57e-07
        233  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        234  |  -5.86e-15   7.85e-08    -0.00   1.000    -1.57e-07    1.57e-07
        235  |   .4788797   .2790124     1.72   0.091    -.0788581    1.036618
        236  |  -5.81e-15   7.87e-08    -0.00   1.000    -1.57e-07    1.57e-07
        237  |          1   7.67e-08  1.3e+07   0.000     .9999998           1
        239  |          1   7.75e-08  1.3e+07   0.000     .9999998           1
        242  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        243  |   .1091598   .1056297     1.03   0.305     -.101991    .3203105
        244  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        246  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        247  |   .4061065   .3285092     1.24   0.221    -.2505741    1.062787
        248  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        250  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        251  |   .1091598   .1025097     1.06   0.291    -.0957542    .3140738
        252  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        270  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        271  |   .0727732   .0791389     0.92   0.361    -.0854233    .2309696
        272  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        274  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        275  |   .0727732   .0791389     0.92   0.361    -.0854233    .2309696
        276  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        278  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        279  |   .0727732   .0791389     0.92   0.361    -.0854233    .2309696
        280  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        282  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
        283  |   .1091598   .1025097     1.06   0.291    -.0957542    .3140738
        284  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
        288  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
             |
         trt |  -.2183196   .0650142    -3.36   0.001    -.3482811    -.088358
       _cons |   5.86e-15   1.18e-07     0.00   1.000    -2.35e-07    2.35e-07
------------------------------------------------------------------------------

. reg fd i.distXtrXdateFes trt2 trt3 trt4 if _merge==1, r cluster(uniqueLocali
> tyID) level(95) //spillover (non-rep vendors)

Linear regression                               Number of obs     =        405
                                                F(35, 61)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.7168
                                                Root MSE          =     .25699

                      (Std. err. adjusted for 62 clusters in uniqueLocalityID)
------------------------------------------------------------------------------
             |               Robust
          fd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          2  |   .1718625   .0700911     2.45   0.017     .0317066    .3120183
          3  |   .2207983   .0669208     3.30   0.002     .0869818    .3546148
          4  |  -2.35e-15   1.43e-07    -0.00   1.000    -2.86e-07    2.86e-07
          5  |   .2829736   .1472831     1.92   0.059    -.0115371    .5774843
          6  |   .2207983   .0669208     3.30   0.002     .0869818    .3546148
          8  |   .2829736   .1472831     1.92   0.059    -.0115371    .5774843
          9  |   .2207983   .0669208     3.30   0.002     .0869818    .3546148
         14  |   .2392159    .075149     3.18   0.002      .088946    .3894857
         17  |   .2392159    .075149     3.18   0.002      .088946    .3894857
         18  |   .2326852   .0708853     3.28   0.002     .0909414    .3744291
         20  |   .2392159    .075149     3.18   0.002      .088946    .3894857
         21  |   .2326852   .0708853     3.28   0.002     .0909414    .3744291
         22  |  -2.36e-15   1.43e-07    -0.00   1.000    -2.86e-07    2.86e-07
         23  |   .1718625   .0700911     2.45   0.017     .0317066    .3120183
         24  |   .2207983   .0669208     3.30   0.002     .0869818    .3546148
         25  |  -2.34e-15   1.43e-07    -0.00   1.000    -2.86e-07    2.86e-07
         26  |   .1718625   .0700911     2.45   0.017     .0317066    .3120183
         27  |   .2207983   .0669208     3.30   0.002     .0869818    .3546148
         28  |  -2.35e-15   8.54e-08    -0.00   1.000    -1.71e-07    1.71e-07
         29  |   .1718625   .0700911     2.45   0.017     .0317066    .3120183
         30  |   .2207983   .0669208     3.30   0.002     .0869818    .3546148
         32  |   .2280259   .0689661     3.31   0.002     .0901196    .3659322
         35  |   .2240636   .0722932     3.10   0.003     .0795045    .3686227
         36  |    .205646   .0700952     2.93   0.005      .065482      .34581
         37  |   .2231757    .066552     3.35   0.001     .0900966    .3562547
         38  |   .4142596    .252841     1.64   0.106    -.0913272    .9198464
         39  |   .2231757    .066552     3.35   0.001     .0900966    .3562547
         40  |   .4142596    .252841     1.64   0.106    -.0913272    .9198464
         41  |   .5681954   .3638133     1.56   0.124    -.1592942    1.295685
         42  |   .4142596    .252841     1.64   0.106    -.0913272    .9198464
         48  |   1.239216    .075149    16.49   0.000     1.088946    1.389486
         50  |   1.239216    .075149    16.49   0.000     1.088946    1.389486
         51  |   .2207983   .0667934     3.31   0.002     .0872366      .35436
         52  |   .2190128   .0727999     3.01   0.004     .0734404    .3645853
         53  |   .2207983   .0667934     3.31   0.002     .0872366      .35436
         54  |   .2392159    .075149     3.18   0.002      .088946    .3894857
         55  |   .2207983   .0667934     3.31   0.002     .0872366      .35436
         56  |   .1642596   .0624883     2.63   0.011     .0393065    .2892127
         57  |   1.234862   .0663752    18.60   0.000     1.102137    1.367587
         58  |   1.119608   .1046253    10.70   0.000     .9103967    1.328819
         61  |   .7089113   .4452485     1.59   0.117     -.181418    1.599241
         62  |   .1392742     .09539     1.46   0.149    -.0514698    .3300182
         63  |   .1163426   .0828295     1.40   0.165    -.0492851    .2819703
         64  |  -2.35e-15   1.43e-07    -0.00   1.000    -2.86e-07    2.86e-07
         65  |   .7089113   .4452485     1.59   0.117     -.181418    1.599241
         66  |   .1392742     .09539     1.46   0.149    -.0514698    .3300182
         67  |   .6163426   .2587075     2.38   0.020      .099025     1.13366
         68  |  -2.35e-15   1.43e-07    -0.00   1.000    -2.86e-07    2.86e-07
         69  |   1.178607   .0706372    16.69   0.000     1.037359    1.319855
         71  |   .6163426   .2587075     2.38   0.020      .099025     1.13366
         72  |  -2.34e-15   9.05e-08    -0.00   1.000    -1.81e-07    1.81e-07
         74  |          1   1.42e-07  7.1e+06   0.000     .9999997           1
         82  |  -2.35e-15   9.05e-08    -0.00   1.000    -1.81e-07    1.81e-07
         89  |   .2089113   .0740643     2.82   0.006     .0608106     .357012
         90  |   .1392742     .09539     1.46   0.149    -.0514698    .3300182
         91  |   .1163426   .0828295     1.40   0.165    -.0492851    .2819703
         92  |  -2.35e-15   1.43e-07    -0.00   1.000    -2.86e-07    2.86e-07
         93  |   .2089113   .0740643     2.82   0.006     .0608106     .357012
         94  |   .1392742     .09539     1.46   0.149    -.0514698    .3300182
         95  |   .1163426   .0828295     1.40   0.165    -.0492851    .2819703
         96  |  -2.33e-15   1.43e-07    -0.00   1.000    -2.86e-07    2.86e-07
         97  |   .2089113   .0740643     2.82   0.006     .0608106     .357012
         98  |   .1392742     .09539     1.46   0.149    -.0514698    .3300182
         99  |   .1163426   .0828295     1.40   0.165    -.0492851    .2819703
        100  |  -2.33e-15   1.43e-07    -0.00   1.000    -2.86e-07    2.86e-07
        101  |   1.208911   .0740643    16.32   0.000     1.060811    1.357012
        102  |          1   1.43e-07  7.0e+06   0.000     .9999997           1
        103  |   1.232685   .0708853    17.39   0.000     1.090941    1.374429
        104  |          1   1.43e-07  7.0e+06   0.000     .9999997           1
        105  |   1.178607   .0706372    16.69   0.000     1.037359    1.319855
        107  |   .4884568   .2875333     1.70   0.094    -.0865016    1.063415
        108  |  -2.31e-15   1.43e-07    -0.00   1.000    -2.86e-07    2.86e-07
        109  |   .2326852   .0708853     3.28   0.002     .0909414    .3744291
        110  |    .216836   .0676718     3.20   0.002     .0815178    .3521541
        111  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        112  |   .2392159    .075149     3.18   0.002      .088946    .3894857
        113  |   .2326852   .0708853     3.28   0.002     .0909414    .3744291
        114  |   .8835026   .3559001     2.48   0.016     .1718364    1.595169
        115  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        116  |   .2392159    .075149     3.18   0.002      .088946    .3894857
        117  |   .2326852   .0708853     3.28   0.002     .0909414    .3744291
        118  |   .8835026   .3559001     2.48   0.016     .1718364    1.595169
        119  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        120  |   .2392159    .075149     3.18   0.002      .088946    .3894857
        137  |   .2326852   .0708853     3.28   0.002     .0909414    .3744291
        138  |   .2392159    .075149     3.18   0.002      .088946    .3894857
        139  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        140  |   .2392159    .075149     3.18   0.002      .088946    .3894857
        141  |   .2326852   .0708853     3.28   0.002     .0909414    .3744291
        142  |   .2392159    .075149     3.18   0.002      .088946    .3894857
        143  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        144  |   .2392159    .075149     3.18   0.002      .088946    .3894857
        145  |   .2326852   .0708853     3.28   0.002     .0909414    .3744291
        146  |   .2359505    .066597     3.54   0.001     .1027815    .3691196
        147  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        148  |   .2392159    .075149     3.18   0.002      .088946    .3894857
        149  |   1.232685   .0708853    17.39   0.000     1.090941    1.374429
        150  |   .7359505   .4661626     1.58   0.120    -.1961991      1.6681
        151  |   1.178607   .0706372    16.69   0.000     1.037359    1.319855
        152  |   1.239216    .075149    16.49   0.000     1.088946    1.389486
        154  |   .2392159    .075149     3.18   0.002      .088946    .3894857
        157  |  -2.34e-15   1.06e-07    -0.00   1.000    -2.13e-07    2.13e-07
        158  |   .1370973   .0861825     1.59   0.117    -.0352352    .3094299
        159  |  -2.32e-15   1.30e-07    -0.00   1.000    -2.60e-07    2.60e-07
        160  |   .4704307   .2904013     1.62   0.110    -.1102625    1.051124
        161  |  -2.37e-15   1.06e-07    -0.00   1.000    -2.13e-07    2.13e-07
        162  |   .1370973   .0861825     1.59   0.117    -.0352352    .3094299
        171  |   .1196079   .1181692     1.01   0.315    -.1166861    .3559019
        172  |   .1370973   .0861825     1.59   0.117    -.0352352    .3094299
        173  |  -2.35e-15   1.06e-07    -0.00   1.000    -2.13e-07    2.13e-07
        174  |   .1370973   .0861825     1.59   0.117    -.0352352    .3094299
        175  |   .1196079   .1181692     1.01   0.315    -.1166861    .3559019
        176  |   .1370973   .0861825     1.59   0.117    -.0352352    .3094299
        177  |          1   1.43e-07  7.0e+06   0.000     .9999997           1
        178  |   .5893034    .384306     1.53   0.130    -.1791639    1.357771
        179  |          1   1.06e-07  9.4e+06   0.000     .9999998           1
        182  |   .5321431    .352904     1.51   0.137     -.173532    1.237818
        184  |   .1988098    .071793     2.77   0.007     .0552509    .3423687
        186  |   .5321431    .352904     1.51   0.137     -.173532    1.237818
        196  |   .1988098    .071793     2.77   0.007     .0552509    .3423687
        198  |   .5321431   .3892094     1.37   0.177    -.2461292    1.310416
        200  |   .1988098    .071793     2.77   0.007     .0552509    .3423687
        202  |   1.178607   .0706372    16.69   0.000     1.037359    1.319855
        205  |    .162627   .0864825     1.88   0.065    -.0103054    .3355593
        206  |    .912627   .2602072     3.51   0.001     .3923105    1.432943
        207  |    .912627   .2602072     3.51   0.001     .3923105    1.432943
        212  |    .162627   .0864825     1.88   0.065    -.0103054    .3355593
        213  |    .162627   .0864825     1.88   0.065    -.0103054    .3355593
        214  |    .162627   .0864825     1.88   0.065    -.0103054    .3355593
        215  |   1.232685   .0708853    17.39   0.000     1.090941    1.374429
        217  |   .1190712   .0824388     1.44   0.154    -.0457754    .2839178
        218  |  -2.36e-15   1.43e-07    -0.00   1.000    -2.86e-07    2.86e-07
        219  |   .7857378   .4308507     1.82   0.073    -.0758013    1.647277
        220  |          1   1.43e-07  7.0e+06   0.000     .9999997           1
        221  |   .7857378   .3282385     2.39   0.020     .1293843    1.442091
        222  |          1   1.43e-07  7.0e+06   0.000     .9999997           1
        227  |  -2.34e-15   1.43e-07    -0.00   1.000    -2.86e-07    2.86e-07
        231  |   .1190712   .0824388     1.44   0.154    -.0457754    .2839178
        232  |  -2.34e-15   1.43e-07    -0.00   1.000    -2.86e-07    2.86e-07
        233  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        234  |  -2.35e-15   1.43e-07    -0.00   1.000    -2.86e-07    2.86e-07
        235  |   .4524045   .2965303     1.53   0.132    -.1405445    1.045354
        236  |  -2.36e-15   1.43e-07    -0.00   1.000    -2.86e-07    2.86e-07
        237  |          1   1.43e-07  7.0e+06   0.000     .9999997           1
        239  |          1   1.43e-07  7.0e+06   0.000     .9999997           1
        242  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        243  |   .1196079   .1166667     1.03   0.309    -.1136816    .3528974
        244  |   .2359505   .0664197     3.55   0.001     .1031362    .3687649
        246  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        247  |    .413072    .330431     1.25   0.216    -.2476657     1.07381
        248  |   .2359505   .0664197     3.55   0.001     .1031362    .3687649
        250  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        251  |   .1196079   .1126674     1.06   0.293    -.1056844    .3449002
        252  |   .2359505   .0664197     3.55   0.001     .1031362    .3687649
        270  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        271  |   .0797386   .0870915     0.92   0.363    -.0944116    .2538888
        272  |   .2359505   .0664197     3.55   0.001     .1031362    .3687649
        274  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        275  |   .0797386   .0870915     0.92   0.363    -.0944116    .2538888
        276  |   .2359505   .0664197     3.55   0.001     .1031362    .3687649
        278  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        279  |   .0797386   .0870915     0.92   0.363    -.0944116    .2538888
        280  |   .2359505   .0664197     3.55   0.001     .1031362    .3687649
        282  |   1.178607   .0706372    16.69   0.000     1.037359    1.319855
        283  |   .1196079   .1126674     1.06   0.293    -.1056844    .3449002
        284  |   1.235951   .0664197    18.61   0.000     1.103136    1.368765
        288  |   .2392159    .075149     3.18   0.002      .088946    .3894857
             |
        trt2 |  -.2326852   .0708853    -3.28   0.002    -.3744291   -.0909414
        trt3 |  -.2392159    .075149    -3.18   0.002    -.3894857    -.088946
        trt4 |  -.1786068   .0706372    -2.53   0.014    -.3198546   -.0373589
       _cons |   2.33e-15          .        .       .            .           .
------------------------------------------------------------------------------

. test _b[trt2]=_b[trt4]

 ( 1)  trt2 - trt4 = 0

       F(  1,    61) =    1.03
            Prob > F =    0.3153

. test _b[trt3]=_b[trt4]

 ( 1)  trt3 - trt4 = 0

       F(  1,    61) =    1.44
            Prob > F =    0.2351

. test _b[trt2]=_b[trt3]

 ( 1)  trt2 - trt3 = 0

       F(  1,    61) =    0.01
            Prob > F =    0.9153

. test _b[trt2] + _b[trt3] =_b[trt4]

 ( 1)  trt2 + trt3 - trt4 = 0

       F(  1,    61) =   11.70
            Prob > F =    0.0011

. 
. reg fdamt i.distXtrXdateFes trt if _merge==1, r cluster(uniqueLocalityID) le
> vel(95) //spillover (non-rep vendors)

Linear regression                               Number of obs     =        411
                                                F(24, 62)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6884
                                                Root MSE          =      .8773

                      (Std. err. adjusted for 63 clusters in uniqueLocalityID)
------------------------------------------------------------------------------
             |               Robust
       fdamt | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          2  |   .5040557   .2129417     2.37   0.021     .0783914    .9297201
          3  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
          4  |  -1.23e-14          .        .       .            .           .
          5  |   .9485002   .5491709     1.73   0.089    -.1492769    2.046277
          6  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
          8  |   .6151668   .2296281     2.68   0.009     .1561469    1.074187
          9  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         14  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         17  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         18  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         20  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         21  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         22  |  -1.23e-14          .        .       .            .           .
         23  |   .5040557   .2129417     2.37   0.021     .0783914    .9297201
         24  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         25  |  -1.23e-14          .        .       .            .           .
         26  |   .5040557   .2129417     2.37   0.021     .0783914    .9297201
         27  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         28  |  -1.24e-14          .        .       .            .           .
         29  |   .5040557   .2129417     2.37   0.021     .0783914    .9297201
         30  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         32  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         35  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         36  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         37  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         38  |   1.736054   1.325721     1.31   0.195    -.9140252    4.386133
         39  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         40  |   1.486054   1.043403     1.42   0.159    -.5996782    3.571786
         41  |    .981405   .4123609     2.38   0.020     .1571072    1.805703
         42  |   .7360537   .2247485     3.28   0.002     .2867879     1.18532
         48  |   5.648072   .2062037    27.39   0.000     5.235876    6.060267
         50  |   10.64807   .2062037    51.64   0.000     10.23588    11.06027
         51  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         52  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         53  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         54  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         55  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         56  |   .4860537    .165051     2.94   0.005     .1561215    .8159859
         57  |   1.648072   .2062037     7.99   0.000     1.235876    2.060267
         58  |   2.324036   .6762926     3.44   0.001     .9721461    3.675926
         61  |   3.148072   2.328591     1.35   0.181    -1.506715    7.802858
         62  |   .4320478   .2851475     1.52   0.135    -.1379539    1.002049
         63  |   .3240358   .2324068     1.39   0.168    -.1405388    .7886105
         64  |  -1.23e-14          .        .       .            .           .
         65  |   2.648072   1.866977     1.42   0.161    -1.083962    6.380105
         66  |   .4320478   .2851475     1.52   0.135    -.1379539    1.002049
         67  |   2.324036   1.108543     2.10   0.040     .1080904    4.539981
         68  |  -1.22e-14          .        .       .            .           .
         69  |   1.648072   .2062037     7.99   0.000     1.235876    2.060267
         71  |   .8240358   .1579163     5.22   0.000     .5083657    1.139706
         72  |  -1.23e-14          .        .       .            .           .
         74  |          5          .        .       .            .           .
         82  |  -1.23e-14          .        .       .            .           .
         89  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         90  |   .4320478   .2851475     1.52   0.135    -.1379539    1.002049
         91  |   .3240358   .2324068     1.39   0.168    -.1405388    .7886105
         92  |  -1.23e-14          .        .       .            .           .
         93  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         94  |   .4320478   .2851475     1.52   0.135    -.1379539    1.002049
         95  |   .3240358   .2324068     1.39   0.168    -.1405388    .7886105
         96  |  -1.22e-14          .        .       .            .           .
         97  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         98  |   .4320478   .2851475     1.52   0.135    -.1379539    1.002049
         99  |   .3240358   .2324068     1.39   0.168    -.1405388    .7886105
        100  |  -1.24e-14          .        .       .            .           .
        101  |   1.648072   .2062037     7.99   0.000     1.235876    2.060267
        102  |          1          .        .       .            .           .
        103  |   1.648072   .2062037     7.99   0.000     1.235876    2.060267
        104  |          1          .        .       .            .           .
        105  |   3.648072   .2062037    17.69   0.000     3.235876    4.060267
        107  |   1.432048   .8646448     1.66   0.103    -.2963525    3.160448
        108  |  -1.22e-14          .        .       .            .           .
        109  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        110  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        111  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        112  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        113  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        114  |   3.314738   1.443213     2.30   0.025     .4297959    6.199681
        115  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        116  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        117  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        118  |   1.314738   .4123609     3.19   0.002     .4904406    2.139036
        119  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        120  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        137  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        138  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        139  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        140  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        141  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        142  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        143  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        144  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        145  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        146  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        147  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        148  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        149  |   1.648072   .2062037     7.99   0.000     1.235876    2.060267
        150  |   1.148072    .507654     2.26   0.027     .1332857    2.162858
        151  |   1.648072   .2062037     7.99   0.000     1.235876    2.060267
        152  |   1.648072   .2062037     7.99   0.000     1.235876    2.060267
        154  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        157  |  -1.23e-14          .        .       .            .           .
        158  |   .4320478   .2679385     1.61   0.112    -.1035537    .9676492
        159  |  -1.23e-14          .        .       .            .           .
        160  |   1.765381   1.206274     1.46   0.148     -.645927    4.176689
        161  |  -1.22e-14          .        .       .            .           .
        162  |   .4320478   .2679385     1.61   0.112    -.1035537    .9676492
        171  |   .3240358    .317821     1.02   0.312    -.3112794     .959351
        172  |   .4320478   .2679385     1.61   0.112    -.1035537    .9676492
        173  |  -1.24e-14          .        .       .            .           .
        174  |   .4320478   .2679385     1.61   0.112    -.1035537    .9676492
        175  |   .3240358    .317821     1.02   0.312    -.3112794     .959351
        176  |   .4320478   .2679385     1.61   0.112    -.1035537    .9676492
        177  |          1          .        .       .            .           .
        178  |   .8240358   .1934477     4.26   0.000     .4373393    1.210732
        179  |          5          .        .       .            .           .
        182  |   2.314738   1.797376     1.29   0.203    -1.278164    5.907641
        184  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        186  |    .981405   .4123609     2.38   0.020     .1571072    1.805703
        196  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        198  |   2.314738   1.797376     1.29   0.203    -1.278164    5.907641
        200  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        202  |   3.648072   .2062037    17.69   0.000     3.235876    4.060267
        205  |   .4860537   .2577838     1.89   0.064    -.0292489    1.001356
        206  |   3.486054   1.070987     3.25   0.002     1.345181    5.626927
        207  |   1.236054   .3074103     4.02   0.000     .6215493    1.850558
        212  |   .4860537   .2577838     1.89   0.064    -.0292489    1.001356
        213  |   .4860537   .2577838     1.89   0.064    -.0292489    1.001356
        214  |   .4860537   .2577838     1.89   0.064    -.0292489    1.001356
        215  |   5.648072   .2062037    27.39   0.000     5.235876    6.060267
        217  |   .4320478   .2778688     1.55   0.125    -.1234041    .9874996
        218  |  -1.22e-14          .        .       .            .           .
        219  |   3.098714   1.675723     1.85   0.069    -.2510079    6.448437
        220  |          4          .        .       .            .           .
        221  |   1.098714   .3394989     3.24   0.002     .4200657    1.777363
        222  |          1          .        .       .            .           .
        227  |  -1.24e-14          .        .       .            .           .
        231  |   .4320478   .2778688     1.55   0.125    -.1234041    .9874996
        232  |  -1.22e-14          .        .       .            .           .
        233  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        234  |  -1.23e-14          .        .       .            .           .
        235  |   2.098714   1.549882     1.35   0.181    -.9994565    5.196885
        236  |  -1.22e-14          .        .       .            .           .
        237  |          3          .        .       .            .           .
        239  |          4          .        .       .            .           .
        242  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        243  |   .3240358   .3193968     1.01   0.314    -.3144294     .962501
        244  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        246  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        247  |   1.549357   1.339418     1.16   0.252    -1.128101    4.226816
        248  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        250  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        251  |   .3240358   .3064543     1.06   0.294    -.2885576    .9366292
        252  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        270  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        271  |   .2160239   .2376512     0.91   0.367    -.2590341    .6910819
        272  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        274  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        275  |   .2160239   .2376512     0.91   0.367    -.2590341    .6910819
        276  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        278  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        279  |   .2160239   .2376512     0.91   0.367    -.2590341    .6910819
        280  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        282  |   1.648072   .2062037     7.99   0.000     1.235876    2.060267
        283  |   .3240358   .3064543     1.06   0.294    -.2885576    .9366292
        284  |   2.648072   .9504159     2.79   0.007     .7482173    4.547926
        288  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
             |
         trt |  -.6480716   .2062037    -3.14   0.003    -1.060267   -.2358762
       _cons |   1.24e-14          .        .       .            .           .
------------------------------------------------------------------------------

. reg fdamt i.distXtrXdateFes trt2 trt3 trt4 if _merge==1, r cluster(uniqueLoc
> alityID) level(95) //spillover (non-rep vendors)

Linear regression                               Number of obs     =        405
                                                F(35, 61)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6902
                                                Root MSE          =     .88876

                      (Std. err. adjusted for 62 clusters in uniqueLocalityID)
------------------------------------------------------------------------------
             |               Robust
       fdamt | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          2  |    .504751   .2206156     2.29   0.026     .0636027    .9458992
          3  |   .6646843   .2061521     3.22   0.002     .2524577    1.076911
          4  |   8.60e-16   2.88e-07     0.00   1.000    -5.77e-07    5.77e-07
          5  |   .9491954   .5577923     1.70   0.094    -.1661793     2.06457
          6  |   .6646843   .2061521     3.22   0.002     .2524577    1.076911
          8  |   .6158621   .2377296     2.59   0.012     .1404924    1.091232
          9  |   .6646843   .2061521     3.22   0.002     .2524577    1.076911
         14  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
         17  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
         18  |   .7203142   .1966734     3.66   0.001     .3270415    1.113587
         20  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
         21  |   .7203142   .1966734     3.66   0.001     .3270415    1.113587
         22  |   8.29e-16   2.88e-07     0.00   1.000    -5.77e-07    5.77e-07
         23  |    .504751   .2206156     2.29   0.026     .0636027    .9458992
         24  |   .6646843   .2061521     3.22   0.002     .2524577    1.076911
         25  |   7.76e-16   2.87e-07     0.00   1.000    -5.74e-07    5.74e-07
         26  |    .504751   .2206156     2.29   0.026     .0636027    .9458992
         27  |   .6646843   .2061521     3.22   0.002     .2524577    1.076911
         28  |   8.36e-16   4.59e-07     0.00   1.000    -9.18e-07    9.18e-07
         29  |    .504751   .2206156     2.29   0.026     .0636027    .9458992
         30  |   .6646843   .2061521     3.22   0.002     .2524577    1.076911
         32  |   .6696272   .2206981     3.03   0.004     .2283141     1.11094
         35  |   .6510839   .2305047     2.82   0.006     .1901612    1.112007
         36  |   .6226548   .2179454     2.86   0.006     .1868461    1.058463
         37  |   .6758103   .2022182     3.34   0.001       .27145    1.080171
         38  |   1.727806   1.352333     1.28   0.206    -.9763517    4.431963
         39  |   .6758103   .2022182     3.34   0.001       .27145    1.080171
         40  |   1.477806   1.067708     1.38   0.171    -.6572094    3.612821
         41  |   1.044581   .4157182     2.51   0.015     .2133007     1.87586
         42  |   .7278056   .2463023     2.95   0.004     .2352938    1.220317
         48  |   5.693113    .242668    23.46   0.000     5.207869    6.178358
         50  |   10.69311    .242668    44.06   0.000     10.20787    11.17836
         51  |   .6646843   .2061107     3.22   0.002     .2525405    1.076828
         52  |   .6370741   .2327988     2.74   0.008     .1715641    1.102584
         53  |   .6646843   .2061107     3.22   0.002     .2525405    1.076828
         54  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
         55  |   .6646843   .2061107     3.22   0.002     .2525405    1.076828
         56  |   .4778056   .1729473     2.76   0.008      .131976    .8236351
         57  |   1.711247   .2007417     8.52   0.000     1.309839    2.112655
         58  |   2.346557   .6684278     3.51   0.001     1.009953    3.683161
         61  |   3.109054   2.273493     1.37   0.176    -1.437075    7.655184
         62  |   .4060363   .2838357     1.43   0.158    -.1615282    .9736008
         63  |   .3601571   .2520074     1.43   0.158    -.1437627    .8640769
         64  |   7.88e-16   2.87e-07     0.00   1.000    -5.74e-07    5.74e-07
         65  |   2.609054   1.807864     1.44   0.154    -1.005994    6.224103
         66  |   .4060363   .2838357     1.43   0.158    -.1615282    .9736008
         67  |   2.360157   1.096967     2.15   0.035      .166635    4.553679
         68  |   8.41e-16   2.88e-07     0.00   1.000    -5.77e-07    5.77e-07
         69  |   1.524995   .2248562     6.78   0.000     1.075368    1.974623
         71  |   .8601571   .1394288     6.17   0.000     .5813519    1.138962
         72  |   8.26e-16   4.64e-07     0.00   1.000    -9.29e-07    9.29e-07
         74  |          5   2.87e-07  1.7e+07   0.000     4.999999    5.000001
         82  |   7.79e-16   4.64e-07     0.00   1.000    -9.29e-07    9.29e-07
         89  |   .6090544   .2319245     2.63   0.011     .1452927    1.072816
         90  |   .4060363   .2838357     1.43   0.158    -.1615282    .9736008
         91  |   .3601571   .2520074     1.43   0.158    -.1437627    .8640769
         92  |   8.73e-16   2.87e-07     0.00   1.000    -5.74e-07    5.74e-07
         93  |   .6090544   .2319245     2.63   0.011     .1452927    1.072816
         94  |   .4060363   .2838357     1.43   0.158    -.1615282    .9736008
         95  |   .3601571   .2520074     1.43   0.158    -.1437627    .8640769
         96  |   9.52e-16   2.87e-07     0.00   1.000    -5.74e-07    5.74e-07
         97  |   .6090544   .2319245     2.63   0.011     .1452927    1.072816
         98  |   .4060363   .2838357     1.43   0.158    -.1615282    .9736008
         99  |   .3601571   .2520074     1.43   0.158    -.1437627    .8640769
        100  |   8.35e-16   2.87e-07     0.00   1.000    -5.74e-07    5.74e-07
        101  |   1.609054   .2319245     6.94   0.000     1.145293    2.072816
        102  |          1   2.88e-07  3.5e+06   0.000     .9999994    1.000001
        103  |   1.720314   .1966734     8.75   0.000     1.327041    2.113587
        104  |          1   2.88e-07  3.5e+06   0.000     .9999994    1.000001
        105  |   3.524995   .2248562    15.68   0.000     3.075368    3.974623
        107  |   1.480209   .8527824     1.74   0.088    -.2250343    3.185453
        108  |   8.49e-16   2.88e-07     0.00   1.000    -5.77e-07    5.77e-07
        109  |   .7203142   .1966734     3.66   0.001     .3270415    1.113587
        110  |    .646141   .2129064     3.03   0.004     .2204084    1.071874
        111  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        112  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
        113  |   .7203142   .1966734     3.66   0.001     .3270415    1.113587
        114  |   3.312808   1.434962     2.31   0.024     .4434237    6.182192
        115  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        116  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
        117  |   .7203142   .1966734     3.66   0.001     .3270415    1.113587
        118  |   1.312808    .398523     3.29   0.002     .5159118    2.109704
        119  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        120  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
        137  |   .7203142   .1966734     3.66   0.001     .3270415    1.113587
        138  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
        139  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        140  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
        141  |   .7203142   .1966734     3.66   0.001     .3270415    1.113587
        142  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
        143  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        144  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
        145  |   .7203142   .1966734     3.66   0.001     .3270415    1.113587
        146  |   .7067138   .2069443     3.41   0.001     .2929032    1.120524
        147  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        148  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
        149  |   1.720314   .1966734     8.75   0.000     1.327041    2.113587
        150  |   1.206714   .5144717     2.35   0.022      .177964    2.235464
        151  |   1.524995   .2248562     6.78   0.000     1.075368    1.974623
        152  |   1.693113    .242668     6.98   0.000     1.207869    2.178358
        154  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
        157  |   8.16e-16   2.63e-07     0.00   1.000    -5.27e-07    5.27e-07
        158  |   .4151032   .2628931     1.58   0.120     -.110584    .9407904
        159  |   9.22e-16   2.65e-07     0.00   1.000    -5.31e-07    5.31e-07
        160  |   1.748437   1.229128     1.42   0.160    -.7093573     4.20623
        161  |   7.93e-16   2.63e-07     0.00   1.000    -5.27e-07    5.27e-07
        162  |   .4151032   .2628931     1.58   0.120     -.110584    .9407904
        171  |   .3465567    .346553     1.00   0.321    -.3464188    1.039532
        172  |   .4151032   .2628931     1.58   0.120     -.110584    .9407904
        173  |   9.21e-16   2.62e-07     0.00   1.000    -5.24e-07    5.24e-07
        174  |   .4151032   .2628931     1.58   0.120     -.110584    .9407904
        175  |   .3465567    .346553     1.00   0.321    -.3464188    1.039532
        176  |   .4151032   .2628931     1.58   0.120     -.110584    .9407904
        177  |          1   2.87e-07  3.5e+06   0.000     .9999994    1.000001
        178  |   .7624977   .2457106     3.10   0.003     .2711691    1.253826
        179  |          5   2.64e-07  1.9e+07   0.000     4.999999    5.000001
        182  |   2.247701   1.772935     1.27   0.210    -1.297501    5.792904
        184  |   .5810348   .2269053     2.56   0.013     .1273095     1.03476
        186  |   .9143681   .3871797     2.36   0.021     .1401544    1.688582
        196  |   .5810348   .2269053     2.56   0.013     .1273095     1.03476
        198  |   2.247701   1.882544     1.19   0.237    -1.516677     6.01208
        200  |   .5810348   .2269053     2.56   0.013     .1273095     1.03476
        202  |   3.524995   .2248562    15.68   0.000     3.075368    3.974623
        205  |   .4846058   .2652692     1.83   0.073    -.0458328    1.015044
        206  |   3.484606   1.054498     3.30   0.002     1.376007    5.593205
        207  |   1.234606   .2898253     4.26   0.000     .6550642    1.814147
        212  |   .4846058   .2652692     1.83   0.073    -.0458328    1.015044
        213  |   .4846058   .2652692     1.83   0.073    -.0458328    1.015044
        214  |   .4846058   .2652692     1.83   0.073    -.0458328    1.015044
        215  |   5.720314   .1966734    29.09   0.000     5.327041    6.113587
        217  |   .3499969   .2488617     1.41   0.165    -.1476327    .8476266
        218  |   8.71e-16   2.88e-07     0.00   1.000    -5.77e-07    5.77e-07
        219  |   3.016664   1.647822     1.83   0.072    -.2783601    6.311687
        220  |          4   2.88e-07  1.4e+07   0.000     3.999999    4.000001
        221  |   1.016664   .3399987     2.99   0.004     .3367942    1.696533
        222  |          1   2.87e-07  3.5e+06   0.000     .9999994    1.000001
        227  |   8.13e-16   2.88e-07     0.00   1.000    -5.77e-07    5.77e-07
        231  |   .3499969   .2488617     1.41   0.165    -.1476327    .8476266
        232  |   8.37e-16   2.88e-07     0.00   1.000    -5.77e-07    5.77e-07
        233  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        234  |   8.56e-16   2.88e-07     0.00   1.000    -5.77e-07    5.77e-07
        235  |   2.016664   1.610755     1.25   0.215    -1.204239    5.237567
        236  |   8.46e-16   2.88e-07     0.00   1.000    -5.77e-07    5.77e-07
        237  |          3   2.86e-07  1.0e+07   0.000     2.999999    3.000001
        239  |          4   2.87e-07  1.4e+07   0.000     3.999999    4.000001
        242  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        243  |   .3465567   .3474297     1.00   0.322     -.348172    1.041285
        244  |   .7067138   .2075629     3.40   0.001     .2916661    1.121762
        246  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        247  |   1.564371   1.350147     1.16   0.251    -1.135415    4.264157
        248  |   .7067138   .2075629     3.40   0.001     .2916661    1.121762
        250  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        251  |   .3465567   .3308124     1.05   0.299    -.3149436    1.008057
        252  |   .7067138   .2075629     3.40   0.001     .2916661    1.121762
        270  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        271  |   .2310378   .2569239     0.90   0.372    -.2827132    .7447888
        272  |   .7067138   .2075629     3.40   0.001     .2916661    1.121762
        274  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        275  |   .2310378   .2569239     0.90   0.372    -.2827132    .7447888
        276  |   .7067138   .2075629     3.40   0.001     .2916661    1.121762
        278  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        279  |   .2310378   .2569239     0.90   0.372    -.2827132    .7447888
        280  |   .7067138   .2075629     3.40   0.001     .2916661    1.121762
        282  |   1.524995   .2248562     6.78   0.000     1.075368    1.974623
        283  |   .3465567   .3308124     1.05   0.299    -.3149436    1.008057
        284  |   2.706714    .946898     2.86   0.006     .8132742    4.600153
        288  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
             |
        trt2 |  -.7203142   .1966734    -3.66   0.001    -1.113587   -.3270415
        trt3 |  -.6931134    .242668    -2.86   0.006    -1.178358   -.2078687
        trt4 |  -.5249954   .2248562    -2.33   0.023    -.9746231   -.0753677
       _cons |  -8.88e-16   3.83e-07    -0.00   1.000    -7.65e-07    7.65e-07
------------------------------------------------------------------------------

. test _b[trt2]=_b[trt4]

 ( 1)  trt2 - trt4 = 0

       F(  1,    61) =    1.84
            Prob > F =    0.1798

. test _b[trt3]=_b[trt4]

 ( 1)  trt3 - trt4 = 0

       F(  1,    61) =    0.99
            Prob > F =    0.3236

. test _b[trt2]=_b[trt3]

 ( 1)  trt2 - trt3 = 0

       F(  1,    61) =    0.03
            Prob > F =    0.8596

. test _b[trt2] + _b[trt3] =_b[trt4]

 ( 1)  trt2 + trt3 - trt4 = 0

       F(  1,    61) =   10.84
            Prob > F =    0.0017

.         
. /*
> reg ihs_fdamt i.distXtrXdateFes trt if _merge==1, r cluster(uniqueLocalityID
> ) level(95) 
> nlcom _b[trt]*xbar*((sqrt(ybar^2+1))/ybar)
> **interpret, ihs: pos and signif => the implied elasticity=0.709
> leebounds ihs_fdamt trt, level(95) cieffect tight() 
> reg ihs_fdamt i.distXtrXdateFes trt2 trt3 trt4 if _merge==1, r cluster(uniqu
> eLocalityID) level(95)
> test _b[trt2]=_b[trt4]
> test _b[trt3]=_b[trt4]
> test _b[trt2]=_b[trt3]
> test _b[trt2] + _b[trt3] =_b[trt4]
> */
. 
. 
. 
. 
. 
. ** Table C.1 ---------------------------------------------------------------
> ----
. *Robustness checks [DIRECT EFFECTS] - Inference, Multiple Testing, Attrition
> , LASSO Estimation
. *REPRESENTATIVE VENDOR
. *POOLED
. *wild cluster bootstrap, pval
. reg fd i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a i.m3
> q1 trt, r cluster(uniqueVendorID) level(95)

Linear regression                               Number of obs     =        335
                                                F(74, 90)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6871
                                                Root MSE          =     .29115

                        (Std. err. adjusted for 91 clusters in uniqueVendorID)
------------------------------------------------------------------------------
             |               Robust
          fd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.1793444   .1167869    -1.54   0.128    -.4113621    .0526732
          5  |   .3993606   .3773529     1.06   0.293    -.3503169    1.149038
          6  |  -.0266847   .1548091    -0.17   0.864      -.33424    .2808706
          7  |  -.1793444   .1167869    -1.54   0.128    -.4113621    .0526732
          8  |   .0005637   .0693092     0.01   0.994    -.1371312    .1382586
         11  |   .0063325    .089923     0.07   0.944    -.1723153    .1849803
         14  |   .0063325    .089923     0.07   0.944    -.1723153    .1849803
         18  |  -.0266847   .1548091    -0.17   0.864      -.33424    .2808706
         22  |  -.1793444   .1167869    -1.54   0.128    -.4113621    .0526732
         23  |  -.0084815   .0669629    -0.13   0.899     -.141515    .1245521
         24  |  -.0266847   .1548091    -0.17   0.864      -.33424    .2808706
         26  |  -.0862906   .1308207    -0.66   0.511    -.3461887    .1736075
         27  |  -.0266847   .1548091    -0.17   0.864      -.33424    .2808706
         29  |  -.0955418    .132337    -0.72   0.472    -.3584525    .1673688
         30  |  -.0266847   .1548091    -0.17   0.864      -.33424    .2808706
         32  |   .1031431   .2009629     0.51   0.609    -.2961047     .502391
         33  |  -.0266847   .1548091    -0.17   0.864      -.33424    .2808706
         34  |  -.1793444   .1167869    -1.54   0.128    -.4113621    .0526732
         35  |  -.0221973   .0649497    -0.34   0.733    -.1512312    .1068366
         37  |  -.0773831   .0776867    -1.00   0.322    -.2317213     .076955
         38  |  -.0057002   .1537024    -0.04   0.970    -.3110568    .2996564
         39  |  -.0707246   .0749631    -0.94   0.348    -.2196519    .0782026
         40  |    .192584   .2193983     0.88   0.382     -.243289     .628457
         41  |   .0609962   .2043907     0.30   0.766    -.3450615     .467054
         42  |   .3021707   .2736264     1.10   0.272     -.241436    .8457774
         51  |  -.0930925   .0734891    -1.27   0.209    -.2390915    .0529065
         52  |  -.2194381    .115415    -1.90   0.060    -.4487302    .0098541
         53  |  -.0757409    .079724    -0.95   0.345    -.2341265    .0826448
         54  |  -.0419175   .1413527    -0.30   0.767    -.3227393    .2389043
         55  |  -.0694364   .0927823    -0.75   0.456    -.2537647    .1148919
         56  |  -.0122097    .118275    -0.10   0.918    -.2471837    .2227643
         57  |   .8351284   .0900564     9.27   0.000     .6562156    1.014041
         58  |   .8201964    .166735     4.92   0.000     .4889481    1.151445
         61  |  -.0767719   .0998652    -0.77   0.444    -.2751716    .1216278
         62  |  -.1314983   .0831401    -1.58   0.117    -.2966706    .0336741
         63  |   -.114972   .1236666    -0.93   0.355    -.3606572    .1307132
         64  |  -.2668609   .1127605    -2.37   0.020    -.4908794   -.0428425
         65  |   .0077154   .1390522     0.06   0.956     -.268536    .2839669
         66  |   .8347112    .121712     6.86   0.000     .5929091    1.076513
         67  |   .4671359   .2301224     2.03   0.045     .0099576    .9243142
         68  |  -.2668609   .1127605    -2.37   0.020    -.4908794   -.0428425
         69  |   .0558884   .1164373     0.48   0.632    -.1754347    .2872115
         71  |   .3207166   .2451542     1.31   0.194     -.166325    .8077582
         72  |  -.2668609   .1127605    -2.37   0.020    -.4908794   -.0428425
         89  |   .0260916   .1063471     0.25   0.807    -.1851854    .2373687
         90  |  -.1434131   .1129321    -1.27   0.207    -.3677724    .0809461
         91  |  -.1733038   .1656309    -1.05   0.298    -.5023585     .155751
         93  |   .0502677   .0925043     0.54   0.588    -.1335083    .2340438
         94  |  -.2098505   .1353133    -1.55   0.124     -.478674     .058973
         95  |  -.1832488   .1316594    -1.39   0.167    -.4448132    .0783155
         97  |  -.0312939   .0855038    -0.37   0.715     -.201162    .1385743
         98  |  -.1314983   .0831401    -1.58   0.117    -.2966706    .0336741
         99  |  -.1503164   .1499785    -1.00   0.319    -.4482748    .1476421
        102  |   .3911878   .5716011     0.68   0.495    -.7443975    1.526773
        103  |   1.062224   .0754935    14.07   0.000     .9122432    1.212205
        104  |   .6440157   .1350621     4.77   0.000     .3756913    .9123401
        107  |   .0125927    .081411     0.15   0.877    -.1491445    .1743298
        109  |  -.1577494   .1006372    -1.57   0.121    -.3576828    .0421839
        110  |  -.0780097   .0942533    -0.83   0.410    -.2652603    .1092409
        113  |  -.0985944   .0798242    -1.24   0.220    -.2571792    .0599903
        114  |  -.1711272   .1851885    -0.92   0.358    -.5390364    .1967821
        117  |  -.0985944   .0798242    -1.24   0.220    -.2571792    .0599903
        118  |  -.2132722   .0984401    -2.17   0.033    -.4088407   -.0177038
        137  |  -.1877178     .09124    -2.06   0.043    -.3689819   -.0064537
        138  |  -.1514245   .1019454    -1.49   0.141    -.3539568    .0511078
        141  |  -.2169045   .0991712    -2.19   0.031    -.4139254   -.0198835
        142  |  -.2172775    .107787    -2.02   0.047    -.4314153   -.0031398
        145  |  -.1277811   .0828624    -1.54   0.127    -.2924018    .0368396
        146  |  -.1679374   .1015517    -1.65   0.102    -.3696876    .0338128
        149  |   .3868122   .4806977     0.80   0.423    -.5681776    1.341802
        150  |   .8530504   .1377969     6.19   0.000     .5792927    1.126808
        153  |  -.0985944   .0798242    -1.24   0.220    -.2571792    .0599903
        154  |  -.0043941    .103672    -0.04   0.966    -.2103565    .2015684
        157  |   -.090423   .1154253    -0.78   0.435    -.3197355    .1388895
        158  |  -.0127429    .164546    -0.08   0.938    -.3396423    .3141565
        159  |  -.0178226   .0706444    -0.25   0.801      -.15817    .1225248
        160  |   .8739813   .1428915     6.12   0.000     .5901024     1.15786
        162  |   .8739813   .1428915     6.12   0.000     .5901024     1.15786
        171  |  -.1630235   .1444841    -1.13   0.262    -.4500664    .1240195
        172  |   .1005329    .120494     0.83   0.406    -.1388494    .3399152
        173  |  -.1630235   .1444841    -1.13   0.262    -.4500664    .1240195
        174  |  -.1260187   .1428915    -0.88   0.380    -.4098976    .1578601
        175  |   -.090423   .1154253    -0.78   0.435    -.3197355    .1388895
        176  |  -.1260187   .1428915    -0.88   0.380    -.4098976    .1578601
        177  |   .8930541   .0851776    10.48   0.000      .723834    1.062274
        178  |   .4872571   .3968732     1.23   0.223    -.3012009    1.275715
        180  |   .4872571   .3968732     1.23   0.223    -.3012009    1.275715
        181  |  -.2355559   .1274889    -1.85   0.068     -.488835    .0177231
        182  |  -.0537621   .0961948    -0.56   0.578    -.2448698    .1373456
        183  |   .8294295   .1103021     7.52   0.000     .6102951    1.048564
        184  |   .2311384   .3947246     0.59   0.560    -.5530509    1.015328
        185  |   -.229558   .1072169    -2.14   0.035     -.442563    -.016553
        186  |  -.0395988   .1140253    -0.35   0.729      -.26613    .1869324
        187  |  -.1705705   .1103021    -1.55   0.126    -.3897049    .0485638
        189  |  -.1705705   .1103021    -1.55   0.126    -.3897049    .0485638
        191  |  -.1705705   .1103021    -1.55   0.126    -.3897049    .0485638
        193  |  -.1705705   .1103021    -1.55   0.126    -.3897049    .0485638
        195  |  -.2060622   .1080225    -1.91   0.060    -.4206678    .0085434
        196  |   .4444138   .4234431     1.05   0.297    -.3968299    1.285657
        197  |    .149147   .4168376     0.36   0.721    -.6789737    .9772677
        198  |  -.1293719   .0883544    -1.46   0.147    -.3049032    .0461594
        199  |  -.1841864   .0912395    -2.02   0.046    -.3654496   -.0029231
        200  |  -.0726646   .0872057    -0.83   0.407    -.2459139    .1005848
        201  |   .7999357   .0924047     8.66   0.000     .6163578    .9835137
        202  |   .9597514   .0665723    14.42   0.000     .8274939    1.092009
        203  |   .8294295   .1103021     7.52   0.000     .6102951    1.048564
        204  |  -.0820888   .0947903    -0.87   0.389    -.2704063    .1062288
        205  |  -.0720387   .0897243    -0.80   0.424    -.2502916    .1062142
        206  |    .447909   .4246911     1.05   0.294    -.3958139    1.291632
        207  |    .170284   .2643886     0.64   0.521    -.3549701     .695538
        210  |  -.0801658   .0893165    -0.90   0.372    -.2576087    .0972771
        211  |  -.1395814   .0974778    -1.43   0.156     -.333238    .0540753
        212  |  -.1210365   .0887659    -1.36   0.176    -.2973855    .0553125
        213  |  -.0936177   .1159567    -0.81   0.422     -.323986    .1367506
        214  |  -.1387672   .1263792    -1.10   0.275    -.3898415     .112307
        215  |   .8789635   .0887659     9.90   0.000     .7026145    1.055313
        216  |   .4503561   .4282354     1.05   0.296    -.4004083    1.301121
        217  |   .2957459   .3527827     0.84   0.404    -.4051184    .9966102
        219  |   .3530052   .3935261     0.90   0.372    -.4288031    1.134814
        221  |   .5906457   .3513669     1.68   0.096     -.107406    1.288697
        227  |  -.1051563   .1095011    -0.96   0.339    -.3226993    .1123868
        231  |  -.0672952   .1038438    -0.65   0.519    -.2735991    .1390087
        233  |   -.021184   .0976363    -0.22   0.829    -.2151556    .1727876
        235  |  -.0806843    .096643    -0.83   0.406    -.2726825    .1113139
        237  |    .973278   .1151159     8.45   0.000     .7445802    1.201976
        239  |   .9640105   .0956111    10.08   0.000     .7740623    1.153959
        241  |  -.0451428   .0726478    -0.62   0.536    -.1894704    .0991848
        243  |  -.3096883   .1145459    -2.70   0.008    -.5372537   -.0821229
        244  |  -.0805531   .0695285    -1.16   0.250    -.2186836    .0575775
        245  |  -.0451428   .0726478    -0.62   0.536    -.1894704    .0991848
        247  |   .4613498   .2840257     1.62   0.108    -.1029168    1.025616
        248  |  -.0791206   .0702223    -1.13   0.263    -.2186294    .0603882
        250  |  -.1424639   .1072858    -1.33   0.188    -.3556059    .0706781
        251  |    .083694    .299493     0.28   0.781     -.511301    .6786891
        252  |  -.0518874   .0669685    -0.77   0.440    -.1849321    .0811572
        267  |  -.1239015   .0826909    -1.50   0.138    -.2881814    .0403784
        269  |  -.0451428   .0726478    -0.62   0.536    -.1894704    .0991848
        271  |  -.3125082   .1611569    -1.94   0.056    -.6326746    .0076582
        272  |  -.1438758   .0868932    -1.66   0.101    -.3165042    .0287527
        274  |  -.1424639   .1072858    -1.33   0.188    -.3556059    .0706781
        275  |  -.2071968   .1551528    -1.34   0.185    -.5154349    .1010412
        276  |  -.0805531   .0695285    -1.16   0.250    -.2186836    .0575775
        278  |  -.1424639   .1072858    -1.33   0.188    -.3556059    .0706781
        279  |  -.1571204     .11033    -1.42   0.158    -.3763101    .0620694
        280  |  -.0518874   .0669685    -0.77   0.440    -.1849321    .0811572
        283  |   -.067997   .0933427    -0.73   0.468    -.2534385    .1174445
        284  |   .8589892   .0896293     9.58   0.000      .680925    1.037053
        285  |  -.1342662   .0949488    -1.41   0.161    -.3228985    .0543662
        287  |  -.1239015   .0826909    -1.50   0.138    -.2881814    .0403784
             |
      fYes_T |   .0891233   .0541565     1.65   0.103     -.018468    .1967147
        mage |  -.0038796   .0032314    -1.20   0.233    -.0102993      .00254
    mmarried |   .0184442   .0708664     0.26   0.795    -.1223442    .1592327
       makan |  -.0642548   .0628405    -1.02   0.309    -.1890985    .0605889
mselfemplo~d |  -.0846515   .0507356    -1.67   0.099    -.1854467    .0161437
       m2q1a |   .0126535   .0173358     0.73   0.467    -.0217871    .0470942
      2.m3q1 |  -.0406722   .0723543    -0.56   0.575    -.1844167    .1030723
         trt |  -.2110939   .0863031    -2.45   0.016      -.38255   -.0396378
       _cons |   .3911605   .1202372     3.25   0.002     .1522883    .6300327
------------------------------------------------------------------------------

. boottest trt, rep($bootstrap_reps) level(95) nogr

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueVendorID, Rademacher weights:
  trt

                           t(90) =    -2.4460
                        Prob>|t| =     0.0080

95% confidence set for null hypothesis expression: [−.396, −.04616]

. reg fdamt i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a i
> .m3q1 trt, r cluster(uniqueVendorID) level(95)

Linear regression                               Number of obs     =        335
                                                F(74, 90)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6626
                                                Root MSE          =     .98339

                        (Std. err. adjusted for 91 clusters in uniqueVendorID)
------------------------------------------------------------------------------
             |               Robust
       fdamt | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.4582819   .3229791    -1.42   0.159    -1.099936    .1833726
          5  |   1.897675   1.555242     1.22   0.226    -1.192084    4.987435
          6  |  -.2000665   .4646267    -0.43   0.668    -1.123129    .7229956
          7  |  -.4582819   .3229791    -1.42   0.159    -1.099936    .1833726
          8  |  -.0708589    .233535    -0.30   0.762    -.5348169    .3930992
         11  |  -.0642205   .2859008    -0.22   0.823    -.6322123    .5037713
         14  |  -.0642205   .2859008    -0.22   0.823    -.6322123    .5037713
         18  |  -.2000665   .4646267    -0.43   0.668    -1.123129    .7229956
         22  |  -.4582819   .3229791    -1.42   0.159    -1.099936    .1833726
         23  |  -.0754331   .2259705    -0.33   0.739    -.5243629    .3734966
         24  |  -.2000665   .4646267    -0.43   0.668    -1.123129    .7229956
         26  |  -.3086423   .3715363    -0.83   0.408    -1.046764    .4294794
         27  |  -.2000665   .4646267    -0.43   0.668    -1.123129    .7229956
         29  |   -.300955   .3771679    -0.80   0.427    -1.050265    .4483549
         30  |  -.2000665   .4646267    -0.43   0.668    -1.123129    .7229956
         32  |  -.0615683   .3466519    -0.18   0.859    -.7502528    .6271161
         33  |  -.2000665   .4646267    -0.43   0.668    -1.123129    .7229956
         34  |  -.4582819   .3229791    -1.42   0.159    -1.099936    .1833726
         35  |  -.1563198    .223856    -0.70   0.487    -.6010488    .2884093
         37  |  -.2914048   .2623388    -1.11   0.270    -.8125866    .2297769
         38  |    .221679   .7246807     0.31   0.760    -1.218026    1.661384
         39  |  -.2463991   .2505101    -0.98   0.328    -.7440812     .251283
         40  |   .8306982   .8672168     0.96   0.341    -.8921794    2.553576
         41  |  -.2246839   .3130756    -0.72   0.475    -.8466632    .3972955
         42  |  -.1586843   .4071062    -0.39   0.698    -.9674719    .6501033
         51  |   -.329452   .2462026    -1.34   0.184    -.8185765    .1596724
         52  |   -.728565   .3803086    -1.92   0.059    -1.484114    .0269844
         53  |  -.3135352   .2612306    -1.20   0.233    -.8325154    .2054451
         54  |  -.2224303   .4650219    -0.48   0.634    -1.146277    .7014169
         55  |  -.2368471   .3103093    -0.76   0.447    -.8533307    .3796366
         56  |  -.1051616   .3592993    -0.29   0.770    -.8189723     .608649
         57  |   1.371158   1.054409     1.30   0.197    -.7236094    3.465926
         58  |   .4256379   .5113153     0.83   0.407    -.5901791    1.441455
         61  |  -.3190787   .3348752    -0.95   0.343    -.9843667    .3462093
         62  |  -.4109146   .2662028    -1.54   0.126    -.9397729    .1179437
         63  |  -.4230628   .3757698    -1.13   0.263    -1.169595    .3234694
         64  |  -.7800383   .3747307    -2.08   0.040    -1.524506   -.0355704
         65  |  -.0330798   .4661105    -0.07   0.944    -.9590897    .8929302
         66  |   3.552302   .3817807     9.30   0.000     2.793828    4.310776
         67  |   1.970753   .9958081     1.98   0.051    -.0075936      3.9491
         68  |  -.7800383   .3747307    -2.08   0.040    -1.524506   -.0355704
         69  |   .1140412   .3413269     0.33   0.739    -.5640643    .7921466
         71  |   .1235593    .291728     0.42   0.673    -.4560092    .7031279
         72  |  -.7800383   .3747307    -2.08   0.040    -1.524506   -.0355704
         89  |   .0549452   .3369338     0.16   0.871    -.6144325    .7243229
         90  |  -.3361518   .3194161    -1.05   0.295    -.9707277     .298424
         91  |   -.586276   .5050997    -1.16   0.249    -1.589745    .4171927
         93  |   .1059689   .2973785     0.36   0.722    -.4848253    .6967632
         94  |  -.6236008   .4062771    -1.53   0.128    -1.430741    .1835395
         95  |  -.6345662   .4187831    -1.52   0.133    -1.466552    .1974195
         97  |  -.1755244   .2666284    -0.66   0.512    -.7052284    .3541795
         98  |  -.4109146   .2662028    -1.54   0.126    -.9397729    .1179437
         99  |  -.5130443   .4516401    -1.14   0.259    -1.410306    .3842176
        102  |   .1534424   .7554691     0.20   0.840    -1.347429    1.654314
        103  |   1.155556   .2369747     4.88   0.000      .684764    1.626347
        104  |  -.1318443   .4422878    -0.30   0.766    -1.010526    .7468375
        107  |  -.0390994    .289731    -0.13   0.893    -.6147005    .5365017
        109  |  -.5561641   .3579146    -1.55   0.124    -1.267224    .1548958
        110  |  -.2787964   .2976886    -0.94   0.352    -.8702068    .3126141
        113  |  -.3262379   .2677346    -1.22   0.226    -.8581395    .2056636
        114  |  -.5203495   .5462577    -0.95   0.343    -1.605586    .5648867
        117  |  -.3262379   .2677346    -1.22   0.226    -.8581395    .2056636
        118  |  -.6368641    .327086    -1.95   0.055    -1.286678    .0129494
        137  |  -.6780439   .3072037    -2.21   0.030    -1.288358   -.0677301
        138  |  -.4881607   .3100566    -1.57   0.119    -1.104142    .1278209
        141  |  -.7860902   .3320658    -2.37   0.020    -1.445797   -.1263834
        142  |  -.5319197   .3389904    -1.57   0.120    -1.205383     .141544
        145  |  -.4342842   .2745476    -1.58   0.117     -.979721    .1111525
        146  |  -.5229768   .2976814    -1.76   0.082    -1.114373    .0684193
        149  |   .6197389   .9616428     0.64   0.521    -1.290732     2.53021
        150  |   .5443544   .4200282     1.30   0.198    -.2901049    1.378814
        153  |  -.3262379   .2677346    -1.22   0.226    -.8581395    .2056636
        154  |  -.0665935   .3556449    -0.19   0.852    -.7731442    .6399573
        157  |  -.2266588   .3800189    -0.60   0.552    -.9816328    .5283151
        158  |  -.0664961    .442106    -0.15   0.881    -.9448169    .8118246
        159  |  -.0185652   .2419088    -0.08   0.939    -.4991593     .462029
        160  |   3.671584   .3878624     9.47   0.000     2.901027     4.44214
        162  |   .6715838   .3878624     1.73   0.087    -.0989727     1.44214
        171  |  -.4347525   .5148858    -0.84   0.401    -1.457663     .588158
        172  |    .195424   .3855605     0.51   0.613    -.5705592    .9614072
        173  |  -.4347525   .5148858    -0.84   0.401    -1.457663     .588158
        174  |  -.3284162   .3878624    -0.85   0.399    -1.098973    .4421402
        175  |  -.2266588   .3800189    -0.60   0.552    -.9816328    .5283151
        176  |  -.3284162   .3878624    -0.85   0.399    -1.098973    .4421402
        177  |   .6296289   .2968852     2.12   0.037     .0398147    1.219443
        178  |   .4335039   .4290525     1.01   0.315    -.4188839    1.285892
        180  |   1.433504   1.261872     1.14   0.259    -1.073426    3.940434
        181  |  -.7591485   .4076957    -1.86   0.066    -1.569107    .0508103
        182  |  -.2596014    .320659    -0.81   0.420    -.8966465    .3774438
        183  |   3.577167   .3095292    11.56   0.000     2.962233    4.192101
        184  |   .8946208   1.553752     0.58   0.566    -2.192178     3.98142
        185  |  -.8339218   .3532763    -2.36   0.020    -1.535767   -.1320767
        186  |  -.2214619   .3981064    -0.56   0.579     -1.01237    .5694459
        187  |  -.4228333   .3095292    -1.37   0.175    -1.037767    .1921005
        189  |  -.4228333   .3095292    -1.37   0.175    -1.037767    .1921005
        191  |  -.4228333   .3095292    -1.37   0.175    -1.037767    .1921005
        193  |  -.4228333   .3095292    -1.37   0.175    -1.037767    .1921005
        195  |  -.5536042   .3385263    -1.64   0.105    -1.226146    .1189373
        196  |   2.228025   2.202446     1.01   0.314    -2.147518    6.603568
        197  |  -.1964414   .4987467    -0.39   0.695    -1.187289     .794406
        198  |  -.5318083   .2906108    -1.83   0.071    -1.109157    .0455408
        199  |  -.5297748   .2856395    -1.85   0.067    -1.097247    .0376979
        200  |  -.3276531   .2952022    -1.11   0.270    -.9141239    .2588177
        201  |   2.371622   .3429724     6.91   0.000     1.690248    3.052997
        202  |   4.819998   .2315877    20.81   0.000     4.359908    5.280087
        203  |   4.577167   .3095292    14.79   0.000     3.962233    5.192101
        204  |  -.3358803   .3078466    -1.09   0.278    -.9474712    .2757107
        205  |  -.2357256   .2986552    -0.79   0.432    -.8290563    .3576051
        206  |    1.78307   1.736049     1.03   0.307    -1.665895    5.232035
        207  |   .8997996   1.363551     0.66   0.511    -1.809133    3.608732
        210  |  -.2656836   .3050219    -0.87   0.386    -.8716628    .3402955
        211  |  -.5002209   .3430505    -1.46   0.148    -1.181751    .1813088
        212  |  -.4202005   .2879737    -1.46   0.148    -.9923104    .1519094
        213  |  -.3647074   .3911364    -0.93   0.354    -1.141768    .4123534
        214  |  -.5572034   .4121332    -1.35   0.180    -1.375978    .2615711
        215  |   .5797995   .2879737     2.01   0.047     .0076896    1.151909
        216  |   1.294603   1.276134     1.01   0.313     -1.24066    3.829866
        217  |   1.438321   1.783898     0.81   0.422    -2.105704    4.982345
        219  |   1.428266   1.523307     0.94   0.351    -1.598049     4.45458
        221  |   .3928598   .4425725     0.89   0.377    -.4863878    1.272107
        227  |  -.4555486   .3750922    -1.21   0.228    -1.200735    .2896375
        231  |  -.3456147   .3641812    -0.95   0.345    -1.069124    .3778947
        233  |  -.1631236   .3199741    -0.51   0.611    -.7988081    .4725609
        235  |  -.3788297   .3220132    -1.18   0.243    -1.018565    .2609056
        237  |   1.824049   .9573342     1.91   0.060    -.0778622    3.725961
        239  |   5.818188   1.100087     5.29   0.000     3.632673    8.003703
        241  |  -.2030678    .248323    -0.82   0.416    -.6964048    .2902691
        243  |  -.8767416   .4021688    -2.18   0.032     -1.67572   -.0777631
        244  |  -.2849281   .2328044    -1.22   0.224    -.7474348    .1775785
        245  |  -.2030678    .248323    -0.82   0.416    -.6964048    .2902691
        247  |   2.093628   1.249934     1.67   0.097    -.3895828     4.57684
        248  |  -.2840778   .2335016    -1.22   0.227    -.7479695    .1798139
        250  |  -.5034807   .3304451    -1.52   0.131    -1.159968    .1530063
        251  |  -.4269928   .3452065    -1.24   0.219    -1.112806    .2588202
        252  |  -.2171516   .2349069    -0.92   0.358    -.6838351    .2495319
        267  |  -.4219012   .2740136    -1.54   0.127    -.9662769    .1224746
        269  |  -.2030678    .248323    -0.82   0.416    -.6964048    .2902691
        271  |  -.9295387   .5040962    -1.84   0.068    -1.931014    .0719365
        272  |  -.5706582   .2888801    -1.98   0.051    -1.144569    .0032525
        274  |  -.5034807   .3304451    -1.52   0.131    -1.159968    .1530063
        275  |  -.6082363   .4840486    -1.26   0.212    -1.569883    .3534107
        276  |  -.2849281   .2328044    -1.22   0.224    -.7474348    .1775785
        278  |  -.5034807   .3304451    -1.52   0.131    -1.159968    .1530063
        279  |  -.5703385   .3687316    -1.55   0.125    -1.302888    .1622112
        280  |  -.2171516   .2349069    -0.92   0.358    -.6838351    .2495319
        283  |  -.2185325   .3151285    -0.69   0.490    -.8445902    .4075252
        284  |   .4310425   .2969736     1.45   0.150    -.1589474    1.021032
        285  |  -.5548738   .3110632    -1.78   0.078    -1.172855    .0631076
        287  |  -.4219012   .2740136    -1.54   0.127    -.9662769    .1224746
             |
      fYes_T |    .351806   .1764955     1.99   0.049     .0011668    .7024452
        mage |  -.0123831   .0108572    -1.14   0.257    -.0339527    .0091866
    mmarried |   .1274287    .241443     0.53   0.599      -.35224    .6070973
       makan |  -.1799835   .2078536    -0.87   0.389    -.5929208    .2329539
mselfemplo~d |  -.3200558   .1889673    -1.69   0.094    -.6954724    .0553607
       m2q1a |   .0478316   .0471618     1.01   0.313    -.0458635    .1415267
      2.m3q1 |   -.082034   .2270069    -0.36   0.719    -.5330228    .3689548
         trt |  -.5505037   .2559821    -2.15   0.034    -1.059057   -.0419505
       _cons |   1.136786   .4283718     2.65   0.009     .2857508    1.987821
------------------------------------------------------------------------------

. boottest trt, rep($bootstrap_reps) level(95) nogr

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueVendorID, Rademacher weights:
  trt

                           t(90) =    -2.1506
                        Prob>|t| =     0.0130

95% confidence set for null hypothesis expression: [−1.024, −.08884]

. reg ihs_fdamt i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q
> 1a i.m3q1 trt, r cluster(uniqueVendorID) level(95)

Linear regression                               Number of obs     =        335
                                                F(74, 90)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6636
                                                Root MSE          =     .51048

                        (Std. err. adjusted for 91 clusters in uniqueVendorID)
------------------------------------------------------------------------------
             |               Robust
   ihs_fdamt | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.2772324   .1789643    -1.55   0.125    -.6327762    .0783114
          5  |   .9594818   .8043675     1.19   0.236    -.6385347    2.557498
          6  |  -.0975332   .2517975    -0.39   0.699    -.5977729    .4027065
          7  |  -.2772324   .1789643    -1.55   0.125    -.6327762    .0783114
          8  |     -.0332   .1228652    -0.27   0.788    -.2772932    .2108932
         11  |  -.0295937   .1524106    -0.19   0.846     -.332384    .2731965
         14  |  -.0295937   .1524106    -0.19   0.846     -.332384    .2731965
         18  |  -.0975332   .2517975    -0.39   0.699    -.5977729    .4027065
         22  |  -.2772324   .1789643    -1.55   0.125    -.6327762    .0783114
         23  |  -.0414445   .1204858    -0.34   0.732    -.2808107    .1979216
         24  |  -.0975332   .2517975    -0.39   0.699    -.5977729    .4027065
         26  |  -.1688184   .2057276    -0.82   0.414    -.5775321    .2398954
         27  |  -.0975332   .2517975    -0.39   0.699    -.5977729    .4027065
         29  |  -.1683103   .2102759    -0.80   0.426    -.5860601    .2494394
         30  |  -.0975332   .2517975    -0.39   0.699    -.5977729    .4027065
         32  |   .0213526   .2195175     0.10   0.923    -.4147572    .4574624
         33  |  -.0975332   .2517975    -0.39   0.699    -.5977729    .4027065
         34  |  -.2772324   .1789643    -1.55   0.125    -.6327762    .0783114
         35  |  -.0732426   .1158072    -0.63   0.529    -.3033138    .1568286
         37  |  -.1498706   .1377405    -1.09   0.279    -.4235161    .1237749
         38  |    .067049   .3357571     0.20   0.842    -.5999912    .7340891
         39  |  -.1310828   .1329339    -0.99   0.327    -.3951793    .1330136
         40  |   .4310805   .4568631     0.94   0.348    -.4765579    1.338719
         41  |    -.05356   .2070831    -0.26   0.797    -.4649667    .3578467
         42  |    .088055   .2524573     0.35   0.728    -.4134955    .5896055
         51  |  -.1755912   .1283842    -1.37   0.175    -.4306489    .0794664
         52  |  -.3877985   .2007085    -1.93   0.056    -.7865409    .0109439
         53  |  -.1516921   .1364297    -1.11   0.269    -.4227336    .1193493
         54  |  -.1029562   .2479053    -0.42   0.679    -.5954633    .3895508
         55  |  -.1256173   .1641257    -0.77   0.446    -.4516817    .2004472
         56  |  -.0439675   .1961486    -0.22   0.823    -.4336509     .345716
         57  |   1.024691   .5018964     2.04   0.044     .0275866    2.021796
         58  |   .5672478      .2743     2.07   0.042     .0223029    1.112193
         61  |  -.1567834   .1749103    -0.90   0.372    -.5042733    .1907064
         62  |  -.2303827   .1382801    -1.67   0.099    -.5051002    .0443348
         63  |  -.2275135   .2043446    -1.11   0.269    -.6334798    .1784528
         64  |  -.4510066   .1970062    -2.29   0.024    -.8423938   -.0596195
         65  |  -.0051549   .2467558    -0.02   0.983    -.4953784    .4850687
         66  |    1.83222   .2026847     9.04   0.000     1.429551    2.234889
         67  |   1.021076   .5129706     1.99   0.050       .00197    2.040181
         68  |  -.4510066   .1970062    -2.29   0.024    -.8423938   -.0596195
         69  |   .0650917   .1866032     0.35   0.728    -.3056282    .4358116
         71  |   .2016028   .2109249     0.96   0.342    -.2174363    .6206419
         72  |  -.4510066   .1970062    -2.29   0.024    -.8423938   -.0596195
         89  |   .0335475    .180557     0.19   0.853    -.3251605    .3922555
         90  |   -.209211   .1755365    -1.19   0.236    -.5579448    .1395228
         91  |  -.3163342   .2746248    -1.15   0.252    -.8619244    .2292559
         93  |   .0661612   .1587419     0.42   0.678    -.2492073    .3815296
         94  |  -.3509756   .2204504    -1.59   0.115    -.7889388    .0869875
         95  |  -.3389449   .2250129    -1.51   0.135    -.7859723    .1080826
         97  |  -.0819781    .142297    -0.58   0.566     -.364676    .2007198
         98  |  -.2303827   .1382801    -1.67   0.099    -.5051002    .0443348
         99  |  -.2746431   .2490078    -1.10   0.273    -.7693406    .2200545
        102  |   .2455057   .5691015     0.43   0.667    -.8851137    1.376125
        103  |   .9689475   .1249117     7.76   0.000     .7207885    1.217106
        104  |   .2534007   .2343714     1.08   0.282    -.2122189    .7190203
        107  |  -.0127851   .1489247    -0.09   0.932      -.30865    .2830798
        109  |  -.2889176   .1848518    -1.56   0.122    -.6561581    .0783228
        110  |  -.1415456   .1603736    -0.88   0.380    -.4601558    .1770645
        113  |     -.1706   .1402297    -1.22   0.227    -.4491908    .1079907
        114  |  -.2807784   .3080917    -0.91   0.365    -.8928562    .3312995
        117  |     -.1706   .1402297    -1.22   0.227    -.4491908    .1079907
        118  |  -.3488662   .1717622    -2.03   0.045    -.6901018   -.0076307
        137  |  -.3475663   .1559826    -2.23   0.028    -.6574529   -.0376797
        138  |  -.2571233   .1660508    -1.55   0.125    -.5870122    .0727656
        141  |  -.4072352   .1714455    -2.38   0.020    -.7478416   -.0666288
        142  |  -.3080416   .1795466    -1.72   0.090    -.6647423    .0486591
        145  |   -.230269   .1443891    -1.59   0.114    -.5171232    .0565852
        146  |  -.2822108   .1597608    -1.77   0.081    -.5996036     .035182
        149  |   .5213832   .6901416     0.76   0.452    -.8497036     1.89247
        150  |   .6373213   .2326746     2.74   0.007     .1750725     1.09957
        153  |     -.1706   .1402297    -1.22   0.227    -.4491908    .1079907
        154  |  -.0131289   .1872462    -0.07   0.944     -.385126    .3588683
        157  |  -.1203361   .2008974    -0.60   0.551    -.5194538    .2787815
        158  |  -.0355273   .2528869    -0.14   0.889    -.5379313    .4668767
        159  |  -.0051782   .1259451    -0.04   0.967    -.2553901    .2450338
        160  |   1.895781    .216545     8.75   0.000     1.465576    2.325985
        162  |   .6824416    .216545     3.15   0.002     .2522372    1.112646
        171  |  -.2354941   .2664681    -0.88   0.379    -.7648796    .2938914
        172  |   .1278774   .2060083     0.62   0.536     -.281394    .5371488
        173  |  -.2354941   .2664681    -0.88   0.379    -.7648796    .2938914
        174  |   -.198932    .216545    -0.92   0.361    -.6291364    .2312725
        175  |  -.1203361   .2008974    -0.60   0.551    -.5194538    .2787815
        176  |   -.198932    .216545    -0.92   0.361    -.6291364    .2312725
        177  |   .6992292   .1480628     4.72   0.000     .4050766    .9933818
        178  |   .4051595   .3344053     1.21   0.229     -.259195    1.069514
        180  |    .873696   .7544068     1.16   0.250    -.6250649    2.372457
        181  |  -.4159395   .2217836    -1.88   0.064    -.8565513    .0246724
        182  |  -.1258373    .168557    -0.75   0.457    -.4607053    .2090307
        183  |     1.8369   .1706043    10.77   0.000     1.497965    2.175835
        184  |   .4797813   .8236327     0.58   0.562    -1.156509    2.116071
        185  |  -.4335982   .1843942    -2.35   0.021    -.7999294   -.0672669
        186  |  -.0959691   .2043072    -0.47   0.640     -.501861    .3099227
        187  |  -.2578125   .1706043    -1.51   0.134    -.5967478    .0811228
        189  |  -.2578125   .1706043    -1.51   0.134    -.5967478    .0811228
        191  |  -.2578125   .1706043    -1.51   0.134    -.5967478    .0811228
        193  |  -.2578125   .1706043    -1.51   0.134    -.5967478    .0811228
        195  |  -.3280466   .1813328    -1.81   0.074    -.6882959    .0322026
        196  |   1.020515   1.000959     1.02   0.311    -.9680642    3.009094
        197  |  -.0104505   .3942898    -0.03   0.979    -.7937761     .772875
        198  |   -.276508   .1534826    -1.80   0.075    -.5814279     .028412
        199  |  -.3042417   .1530731    -1.99   0.050    -.6083481   -.0001353
        200  |   -.163505   .1575052    -1.04   0.302    -.4764166    .1494067
        201  |   1.472741   .1709827     8.61   0.000     1.133054    1.812428
        202  |   2.212897   .1203016    18.39   0.000     1.973896    2.451897
        203  |   2.054626   .1706043    12.04   0.000      1.71569    2.393561
        204  |  -.1855736   .1640686    -1.13   0.261    -.5115246    .1403774
        205  |  -.1214312   .1594092    -0.76   0.448    -.4381255     .195263
        206  |    .945129   .9004875     1.05   0.297    -.8438467    2.734105
        207  |   .4124434   .6211893     0.66   0.508    -.8216575    1.646544
        210  |  -.1380986   .1615653    -0.85   0.395    -.4590763     .182879
        211  |  -.2560761   .1771206    -1.45   0.152    -.6079571    .0958048
        212  |  -.2219166   .1524661    -1.46   0.149    -.5248171    .0809839
        213  |  -.1762833   .2075057    -0.85   0.398    -.5885295     .235963
        214  |  -.2729551   .2215443    -1.23   0.221    -.7130916    .1671814
        215  |    .659457   .1524661     4.33   0.000     .3565565    .9623575
        216  |   .8132344   .7776483     1.05   0.298    -.7316997    2.358169
        217  |   .6682184   .8279422     0.81   0.422    -.9766333     2.31307
        219  |   .7593179   .7928367     0.96   0.341    -.8157906    2.334426
        221  |   .4437621   .3285162     1.35   0.180    -.2088927    1.096417
        227  |  -.2109243   .1959373    -1.08   0.285    -.6001879    .1783393
        231  |  -.1615831   .1886174    -0.86   0.394    -.5363045    .2131382
        233  |  -.0797882   .1707181    -0.47   0.641    -.4189496    .2593732
        235  |  -.1855833   .1715665    -1.08   0.282    -.5264301    .1552636
        237  |   1.259725    .440154     2.86   0.005     .3852823    2.134168
        239  |   2.390048   .2710592     8.82   0.000     1.851541    2.928554
        241  |  -.1120185   .1301035    -0.86   0.392    -.3704918    .1464548
        243  |  -.4942926   .2077521    -2.38   0.019    -.9070284   -.0815569
        244  |  -.1531332    .120043    -1.28   0.205    -.3916196    .0853532
        245  |  -.1120185   .1301035    -0.86   0.392    -.3704918    .1464548
        247  |   1.075417   .6380884     1.69   0.095    -.1922566    2.343092
        248  |  -.1524285   .1205828    -1.26   0.209    -.3919874    .0871303
        250  |  -.2676994   .1778796    -1.50   0.136    -.6210883    .0856895
        251  |  -.1230778   .2469241    -0.50   0.619    -.6136355    .3674799
        252  |  -.1203709   .1217544    -0.99   0.325    -.3622573    .1215154
        267  |  -.2233259    .144106    -1.55   0.125    -.5096177    .0629659
        269  |  -.1120185   .1301035    -0.86   0.392    -.3704918    .1464548
        271  |  -.5136405   .2698264    -1.90   0.060    -1.049698    .0224168
        272  |  -.2987465   .1510613    -1.98   0.051    -.5988561    .0013631
        274  |  -.2676994   .1778796    -1.50   0.136    -.6210883    .0856895
        275  |  -.3339561   .2623301    -1.27   0.206    -.8551206    .1872084
        276  |  -.1531332    .120043    -1.28   0.205    -.3916196    .0853532
        278  |  -.2676994   .1778796    -1.50   0.136    -.6210883    .0856895
        279  |  -.2898089   .1939021    -1.49   0.139    -.6750293    .0954114
        280  |  -.1203709   .1217544    -0.99   0.325    -.3622573    .1215154
        283  |  -.1128427   .1684028    -0.67   0.505    -.4474042    .2217188
        284  |   .5840364   .1566088     3.73   0.000     .2729056    .8951672
        285  |  -.2889848   .1656386    -1.74   0.084    -.6180547    .0400851
        287  |  -.2233259    .144106    -1.55   0.125    -.5096177    .0629659
             |
      fYes_T |   .1769663   .0888941     1.99   0.050     .0003627    .3535698
        mage |  -.0069431   .0056746    -1.22   0.224    -.0182166    .0043304
    mmarried |   .0538371   .1261322     0.43   0.671    -.1967465    .3044208
       makan |  -.0890688   .1091163    -0.82   0.416    -.3058474    .1277098
mselfemplo~d |  -.1723649   .0962772    -1.79   0.077    -.3636365    .0189066
       m2q1a |   .0263629   .0264459     1.00   0.322    -.0261764    .0789023
      2.m3q1 |  -.0519374   .1209513    -0.43   0.669    -.2922284    .1883535
         trt |  -.3236926   .1384358    -2.34   0.022    -.5987194   -.0486658
       _cons |   .6400971   .2210431     2.90   0.005     .2009564    1.079238
------------------------------------------------------------------------------

. boottest trt, rep($bootstrap_reps) level(95) nogr

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueVendorID, Rademacher weights:
  trt

                           t(90) =    -2.3382
                        Prob>|t| =     0.0050

95% confidence set for null hypothesis expression: [−.5799, −.08923]

. *randomization inf: permuntation test, pval
. ritest trt _b[trt], reps($bootstrap_reps) cluster(uniqueVendorID) strata(ge0
> 1) seed(546): reg fd i.distXtrXdateFes fYes_T mage mmarried makan mselfemplo
> yed m2q1a i.m3q1 trt
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       335
-------------+----------------------------------   F(157, 177)     =      2.48
       Model |  32.9544319       157   .20990084   Prob > F        =    0.0000
    Residual |   15.003777       177  .084767102   R-squared       =    0.6871
-------------+----------------------------------   Adj R-squared   =    0.4096
       Total |   47.958209       334  .143587452   Root MSE        =    .29115

------------------------------------------------------------------------------
          fd | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.1793444   .3429397    -0.52   0.602    -.8561212    .4974324
          5  |   .3993606   .2500339     1.60   0.112    -.0940706    .8927917
          6  |  -.0266847   .3539781    -0.08   0.940    -.7252454     .671876
          7  |  -.1793444   .3429397    -0.52   0.602    -.8561212    .4974324
          8  |   .0005637   .2271246     0.00   0.998     -.447657    .4487844
         11  |   .0063325   .3445497     0.02   0.985    -.6736217    .6862867
         14  |   .0063325   .3445497     0.02   0.985    -.6736217    .6862867
         18  |  -.0266847   .3539781    -0.08   0.940    -.7252454     .671876
         22  |  -.1793444   .3429397    -0.52   0.602    -.8561212    .4974324
         23  |  -.0084815   .2402036    -0.04   0.972    -.4825129      .46555
         24  |  -.0266847   .3539781    -0.08   0.940    -.7252454     .671876
         26  |  -.0862906   .2235527    -0.39   0.700    -.5274623    .3548811
         27  |  -.0266847   .3539781    -0.08   0.940    -.7252454     .671876
         29  |  -.0955418   .2285484    -0.42   0.676    -.5465723    .3554887
         30  |  -.0266847   .3539781    -0.08   0.940    -.7252454     .671876
         32  |   .1031431   .2134006     0.48   0.629    -.3179938    .5242801
         33  |  -.0266847   .3539781    -0.08   0.940    -.7252454     .671876
         34  |  -.1793444   .3429397    -0.52   0.602    -.8561212    .4974324
         35  |  -.0221973   .2453625    -0.09   0.928    -.5064097     .462015
         37  |  -.0773831   .2002001    -0.39   0.700    -.4724695    .3177032
         38  |  -.0057002   .2095677    -0.03   0.978     -.419273    .4078726
         39  |  -.0707246   .2398981    -0.29   0.768    -.5441534    .4027041
         40  |    .192584   .2054721     0.94   0.350    -.2129064    .5980744
         41  |   .0609962   .2115445     0.29   0.773    -.3564778    .4784703
         42  |   .3021707   .2322151     1.30   0.195     -.156096    .7604373
         51  |  -.0930925   .2404769    -0.39   0.699    -.5676634    .3814783
         52  |  -.2194381    .245071    -0.90   0.372    -.7030752    .2641991
         53  |  -.0757409   .2437028    -0.31   0.756    -.5566779    .4051962
         54  |  -.0419175   .2471862    -0.17   0.866     -.529729    .4458939
         55  |  -.0694364   .2257963    -0.31   0.759    -.5150356    .3761629
         56  |  -.0122097   .2457091    -0.05   0.960    -.4971062    .4726868
         57  |   .8351284   .2744079     3.04   0.003     .2935962    1.376661
         58  |   .8201964   .2708457     3.03   0.003     .2856939    1.354699
         61  |  -.0767719   .2455542    -0.31   0.755    -.5613626    .4078188
         62  |  -.1314983   .2705732    -0.49   0.628    -.6654628    .4024663
         63  |   -.114972   .2472957    -0.46   0.643    -.6029994    .3730554
         64  |  -.2668609   .3441315    -0.78   0.439    -.9459897    .4122678
         65  |   .0077154    .243507     0.03   0.975    -.4728352    .4882661
         66  |   .8347112   .2751204     3.03   0.003      .291773    1.377649
         67  |   .4671359    .220842     2.12   0.036     .0313138    .9029581
         68  |  -.2668609   .3441315    -0.78   0.439    -.9459897    .4122678
         69  |   .0558884   .3442092     0.16   0.871    -.6233937    .7351706
         71  |   .3207166   .2182466     1.47   0.143    -.1099838    .7514171
         72  |  -.2668609   .3441315    -0.78   0.439    -.9459897    .4122678
         89  |   .0260916   .2410743     0.11   0.914    -.4496582    .5018415
         90  |  -.1434131   .3426553    -0.42   0.676    -.8196287    .5328024
         91  |  -.1733038   .2519212    -0.69   0.492    -.6704595     .323852
         93  |   .0502677   .2287712     0.22   0.826    -.4012025     .501738
         94  |  -.2098505   .2778274    -0.76   0.451     -.758131    .3384301
         95  |  -.1832488   .2344566    -0.78   0.436    -.6459389    .2794413
         97  |  -.0312939   .2284234    -0.14   0.891    -.4820778      .41949
         98  |  -.1314983   .2705732    -0.49   0.628    -.6654628    .4024663
         99  |  -.1503164   .2347536    -0.64   0.523    -.6135925    .3129597
        102  |   .3911878   .2705331     1.45   0.150    -.1426978    .9250733
        103  |   1.062224   .2712598     3.92   0.000     .5269046    1.597544
        104  |   .6440157   .3506659     1.84   0.068    -.0480085     1.33604
        107  |   .0125927    .245944     0.05   0.959    -.4727672    .4979526
        109  |  -.1577494   .2707031    -0.58   0.561    -.6919704    .3764715
        110  |  -.0780097   .2287964    -0.34   0.734    -.5295295    .3735102
        113  |  -.0985944    .338683    -0.29   0.771    -.7669709     .569782
        114  |  -.1711272   .2713689    -0.63   0.529    -.7066621    .3644077
        117  |  -.0985944    .338683    -0.29   0.771    -.7669709     .569782
        118  |  -.2132722   .2276338    -0.94   0.350    -.6624979    .2359534
        137  |  -.1877178   .3433202    -0.55   0.585    -.8652455      .48981
        138  |  -.1514245   .2448801    -0.62   0.537    -.6346849    .3318359
        141  |  -.2169045   .3446876    -0.63   0.530    -.8971307    .4633218
        142  |  -.2172775   .3430599    -0.63   0.527    -.8942916    .4597366
        145  |  -.1277811   .3396394    -0.38   0.707     -.798045    .5424828
        146  |  -.1679374   .2457685    -0.68   0.495     -.652951    .3170762
        149  |   .3868122   .2691827     1.44   0.152    -.1444083    .9180328
        150  |   .8530504   .2420844     3.52   0.001     .3753071    1.330794
        153  |  -.0985944    .338683    -0.29   0.771    -.7669709     .569782
        154  |  -.0043941    .342787    -0.01   0.990    -.6808695    .6720814
        157  |   -.090423   .2707681    -0.33   0.739    -.6247722    .4439262
        158  |  -.0127429   .2785035    -0.05   0.964    -.5623577    .5368719
        159  |  -.0178226   .3387921    -0.05   0.958    -.6864143    .6507692
        160  |   .8739813   .3497615     2.50   0.013     .1837418    1.564221
        162  |   .8739813   .3497615     2.50   0.013     .1837418    1.564221
        171  |  -.1630235   .3477969    -0.47   0.640    -.8493858    .5233388
        172  |   .1005329   .3475972     0.29   0.773    -.5854353    .7865011
        173  |  -.1630235   .3477969    -0.47   0.640    -.8493858    .5233388
        174  |  -.1260187   .3497615    -0.36   0.719    -.8162582    .5642207
        175  |   -.090423   .2707681    -0.33   0.739    -.6247722    .4439262
        176  |  -.1260187   .3497615    -0.36   0.719    -.8162582    .5642207
        177  |   .8930541   .3430995     2.60   0.010     .2159619    1.570146
        178  |   .4872571   .2785035     1.75   0.082    -.0623577    1.036872
        180  |   .4872571   .2785035     1.75   0.082    -.0623577    1.036872
        181  |  -.2355559   .2717164    -0.87   0.387    -.7717767    .3006648
        182  |  -.0537621    .244333    -0.22   0.826    -.5359429    .4284186
        183  |   .8294295   .3419884     2.43   0.016       .15453    1.504329
        184  |   .2311384   .2495051     0.93   0.356    -.2612493     .723526
        185  |   -.229558   .3461258    -0.66   0.508    -.9126225    .4535066
        186  |  -.0395988   .2735967    -0.14   0.885    -.5795302    .5003327
        187  |  -.1705705   .3419884    -0.50   0.619      -.84547    .5043289
        189  |  -.1705705   .3419884    -0.50   0.619      -.84547    .5043289
        191  |  -.1705705   .3419884    -0.50   0.619      -.84547    .5043289
        193  |  -.1705705   .3419884    -0.50   0.619      -.84547    .5043289
        195  |  -.2060622   .2727316    -0.76   0.451    -.7442863    .3321619
        196  |   .4444138   .2712492     1.64   0.103     -.090885    .9797125
        197  |    .149147   .2419903     0.62   0.538    -.3284105    .6267045
        198  |  -.1293719   .3444743    -0.38   0.708    -.8091772    .5504334
        199  |  -.1841864   .2419903    -0.76   0.448    -.6617439    .2933712
        200  |  -.0726646   .2292851    -0.32   0.752    -.5251489    .3798198
        201  |   .7999357   .2711732     2.95   0.004      .264787    1.335084
        202  |   .9597514   .3389269     2.83   0.005     .2908938    1.628609
        203  |   .8294295   .3419884     2.43   0.016       .15453    1.504329
        204  |  -.0820888   .3431225    -0.24   0.811    -.7592264    .5950489
        205  |  -.0720387   .2166769    -0.33   0.740    -.4996413     .355564
        206  |    .447909   .2741775     1.63   0.104    -.0931686    .9889866
        207  |    .170284     .23182     0.73   0.464    -.2872029    .6277708
        210  |  -.0801658   .2413793    -0.33   0.740    -.5565175    .3961859
        211  |  -.1395814   .2444932    -0.57   0.569    -.6220782    .3429154
        212  |  -.1210365   .3407201    -0.36   0.723    -.7934331    .5513601
        213  |  -.0936177   .2462604    -0.38   0.704    -.5796021    .3923667
        214  |  -.1387672   .2798616    -0.50   0.621    -.6910622    .4135277
        215  |   .8789635   .3407201     2.58   0.011     .2065669     1.55136
        216  |   .4503561   .2730573     1.65   0.101    -.0885109    .9892231
        217  |   .2957459   .2468492     1.20   0.232    -.1914005    .7828923
        219  |   .3530052   .2391041     1.48   0.142    -.1188565     .824867
        221  |   .5906457   .2443361     2.42   0.017      .108459    1.072832
        227  |  -.1051563   .3480322    -0.30   0.763     -.791983    .5816704
        231  |  -.0672952   .2495832    -0.27   0.788    -.5598369    .4252465
        233  |   -.021184    .246948    -0.09   0.932    -.5085253    .4661573
        235  |  -.0806843   .2335045    -0.35   0.730    -.5414953    .3801268
        237  |    .973278   .2769514     3.51   0.001     .4267263     1.51983
        239  |   .9640105    .272629     3.54   0.001      .425989    1.502032
        241  |  -.0451428   .3399693    -0.13   0.895    -.7160577     .625772
        243  |  -.3096883   .3434601    -0.90   0.368     -.987492    .3681155
        244  |  -.0805531   .2674128    -0.30   0.764    -.6082808    .4471746
        245  |  -.0451428   .3399693    -0.13   0.895    -.7160577     .625772
        247  |   .4613498   .2436695     1.89   0.060    -.0195215    .9422211
        248  |  -.0791206   .2675177    -0.30   0.768    -.6070553    .4488142
        250  |  -.1424639   .3442826    -0.41   0.680    -.8218908     .536963
        251  |    .083694   .2475861     0.34   0.736    -.4049065    .5722945
        252  |  -.0518874   .3388011    -0.15   0.878     -.720497    .6167221
        267  |  -.1239015   .3395845    -0.36   0.716     -.794057     .546254
        269  |  -.0451428   .3399693    -0.13   0.895    -.7160577     .625772
        271  |  -.3125082    .280822    -1.11   0.267    -.8666984    .2416819
        272  |  -.1438758   .3437486    -0.42   0.676    -.8222489    .5344973
        274  |  -.1424639   .3442826    -0.41   0.680    -.8218908     .536963
        275  |  -.2071968   .2473011    -0.84   0.403    -.6952349    .2808412
        276  |  -.0805531   .2674128    -0.30   0.764    -.6082808    .4471746
        278  |  -.1424639   .3442826    -0.41   0.680    -.8218908     .536963
        279  |  -.1571204   .2749415    -0.57   0.568    -.6997056    .3854648
        280  |  -.0518874   .3388011    -0.15   0.878     -.720497    .6167221
        283  |   -.067997   .2687932    -0.25   0.801    -.5984488    .4624548
        284  |   .8589892   .3443496     2.49   0.014       .17943    1.538548
        285  |  -.1342662    .345712    -0.39   0.698    -.8165141    .5479817
        287  |  -.1239015   .3395845    -0.36   0.716     -.794057     .546254
             |
      fYes_T |   .0891233   .0577389     1.54   0.124    -.0248219    .2030686
        mage |  -.0038796    .003237    -1.20   0.232    -.0102676    .0025084
    mmarried |   .0184442   .0574287     0.32   0.748    -.0948888    .1317773
       makan |  -.0642548   .0526072    -1.22   0.224    -.1680729    .0395633
mselfemplo~d |  -.0846515   .0452292    -1.87   0.063    -.1739094    .0046064
       m2q1a |   .0126535   .0130981     0.97   0.335     -.013195     .038502
      2.m3q1 |  -.0406722   .0659494    -0.62   0.538    -.1708204    .0894761
         trt |  -.2110939   .0565693    -3.73   0.000    -.3227309   -.0994568
       _cons |   .3911605   .1923626     2.03   0.043     .0115411    .7707799
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000

      Command: regress fd i.distXtrXdateFes fYes_T mage mmarried makan
                   mselfemployed m2q1a i.m3q1 trt
        _pm_1: _b[trt]
  res. var(s):  trt
   Resampling:  Permuting trt
Clust. var(s):  uniqueVendorID
     Clusters:  207
Strata var(s):  ge01
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |  -.2110939       2    1000  0.0020  0.0014  .0002423   .0072058
------------------------------------------------------------------------------
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. ritest trt _b[trt], reps($bootstrap_reps) cluster(uniqueVendorID) strata(ge0
> 1) seed(546): reg fdamt i.distXtrXdateFes fYes_T mage mmarried makan mselfem
> ployed m2q1a i.m3q1 trt
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       335
-------------+----------------------------------   F(157, 177)     =      2.21
       Model |  336.185414       157  2.14130837   Prob > F        =    0.0000
    Residual |   171.16981       177  .967061075   R-squared       =    0.6626
-------------+----------------------------------   Adj R-squared   =    0.3634
       Total |  507.355224       334  1.51902762   Root MSE        =    .98339

------------------------------------------------------------------------------
       fdamt | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.4582819   1.158327    -0.40   0.693     -2.74419    1.827626
          5  |   1.897675   .8445243     2.25   0.026     .2310428    3.564308
          6  |  -.2000665   1.195611    -0.17   0.867    -2.559553     2.15942
          7  |  -.4582819   1.158327    -0.40   0.693     -2.74419    1.827626
          8  |  -.0708589    .767145    -0.09   0.927    -1.584787    1.443069
         11  |  -.0642205   1.163765    -0.06   0.956    -2.360861     2.23242
         14  |  -.0642205   1.163765    -0.06   0.956    -2.360861     2.23242
         18  |  -.2000665   1.195611    -0.17   0.867    -2.559553     2.15942
         22  |  -.4582819   1.158327    -0.40   0.693     -2.74419    1.827626
         23  |  -.0754331    .811321    -0.09   0.926     -1.67654    1.525674
         24  |  -.2000665   1.195611    -0.17   0.867    -2.559553     2.15942
         26  |  -.3086423   .7550804    -0.41   0.683    -1.798761    1.181477
         27  |  -.2000665   1.195611    -0.17   0.867    -2.559553     2.15942
         29  |   -.300955   .7719541    -0.39   0.697    -1.824373    1.222463
         30  |  -.2000665   1.195611    -0.17   0.867    -2.559553     2.15942
         32  |  -.0615683   .7207903    -0.09   0.932    -1.484017     1.36088
         33  |  -.2000665   1.195611    -0.17   0.867    -2.559553     2.15942
         34  |  -.4582819   1.158327    -0.40   0.693     -2.74419    1.827626
         35  |  -.1563198    .828746    -0.19   0.851    -1.791814    1.479175
         37  |  -.2914048   .6762038    -0.43   0.667    -1.625864    1.043054
         38  |    .221679    .707844     0.31   0.755    -1.175221    1.618579
         39  |  -.2463991   .8102894    -0.30   0.761    -1.845471    1.352672
         40  |   .8306982   .6940107     1.20   0.233    -.5389023    2.200299
         41  |  -.2246839   .7145211    -0.31   0.754    -1.634761    1.185393
         42  |  -.1586843    .784339    -0.20   0.840    -1.706544    1.389175
         51  |   -.329452   .8122442    -0.41   0.686    -1.932381    1.273477
         52  |   -.728565   .8277615    -0.88   0.380    -2.362117    .9049869
         53  |  -.3135352   .8231402    -0.38   0.704    -1.937967    1.310897
         54  |  -.2224303    .834906    -0.27   0.790    -1.870081    1.425221
         55  |  -.2368471   .7626583    -0.31   0.757    -1.741921    1.268227
         56  |  -.1051616   .8299169    -0.13   0.899    -1.742967    1.532644
         57  |   1.371158   .9268508     1.48   0.141    -.4579424    3.200259
         58  |   .4256379   .9148192     0.47   0.642    -1.379719    2.230995
         61  |  -.3190787   .8293935    -0.38   0.701    -1.955851    1.317694
         62  |  -.4109146   .9138985    -0.45   0.654    -2.214454    1.392625
         63  |  -.4230628   .8352756    -0.51   0.613    -2.071443    1.225318
         64  |  -.7800383   1.162352    -0.67   0.503     -3.07389    1.513814
         65  |  -.0330798   .8224789    -0.04   0.968    -1.656207    1.590047
         66  |   3.552302   .9292573     3.82   0.000     1.718453    5.386152
         67  |   1.970753   .7459245     2.64   0.009     .4987029    3.442803
         68  |  -.7800383   1.162352    -0.67   0.503     -3.07389    1.513814
         69  |   .1140412   1.162615     0.10   0.922    -2.180329    2.408411
         71  |   .1235593   .7371585     0.17   0.867    -1.331191     1.57831
         72  |  -.7800383   1.162352    -0.67   0.503     -3.07389    1.513814
         89  |   .0549452   .8142622     0.07   0.946    -1.551966    1.661857
         90  |  -.3361518   1.157366    -0.29   0.772    -2.620164     1.94786
         91  |   -.586276    .850899    -0.69   0.492    -2.265489    1.092937
         93  |   .1059689   .7727068     0.14   0.891    -1.418935    1.630873
         94  |  -.6236008   .9384009    -0.66   0.507    -2.475495    1.228293
         95  |  -.6345662     .79191    -0.80   0.424    -2.197367    .9282342
         97  |  -.1755244    .771532    -0.23   0.820     -1.69811    1.347061
         98  |  -.4109146   .9138985    -0.45   0.654    -2.214454    1.392625
         99  |  -.5130443   .7929129    -0.65   0.518    -2.077824    1.051735
        102  |   .1534424   .9137634     0.17   0.867    -1.649831    1.956715
        103  |   1.155556   .9162178     1.26   0.209     -.652561    2.963672
        104  |  -.1318443   1.184423    -0.11   0.911    -2.469253    2.205564
        107  |  -.0390994     .83071    -0.05   0.963     -1.67847    1.600271
        109  |  -.5561641   .9143375    -0.61   0.544     -2.36057    1.248242
        110  |  -.2787964   .7727916    -0.36   0.719    -1.803868    1.246275
        113  |  -.3262379   1.143949    -0.29   0.776    -2.583773    1.931297
        114  |  -.5203495   .9165863    -0.57   0.571    -2.329193    1.288494
        117  |  -.3262379   1.143949    -0.29   0.776    -2.583773    1.931297
        118  |  -.6368641    .768865    -0.83   0.409    -2.154186    .8804581
        137  |  -.6780439   1.159612    -0.58   0.559    -2.966488    1.610401
        138  |  -.4881607   .8271167    -0.59   0.556     -2.12044    1.144119
        141  |  -.7860902    1.16423    -0.68   0.500    -3.083649    1.511469
        142  |  -.5319197   1.158733    -0.46   0.647    -2.818629     1.75479
        145  |  -.4342842    1.14718    -0.38   0.705    -2.698194    1.829626
        146  |  -.5229768   .8301174    -0.63   0.530    -2.161178    1.115224
        149  |   .6197389   .9092021     0.68   0.496    -1.174532     2.41401
        150  |   .5443544   .8176739     0.67   0.506     -1.06929    2.157999
        153  |  -.3262379   1.143949    -0.29   0.776    -2.583773    1.931297
        154  |  -.0665935   1.157811    -0.06   0.954    -2.351484    2.218297
        157  |  -.2266588   .9145569    -0.25   0.805    -2.031498     1.57818
        158  |  -.0664961   .9406845    -0.07   0.944    -1.922897    1.789904
        159  |  -.0185652   1.144318    -0.02   0.987    -2.276827    2.239697
        160  |   3.671584   1.181368     3.11   0.002     1.340204    6.002964
        162  |   .6715838   1.181368     0.57   0.570    -1.659796    3.002964
        171  |  -.4347525   1.174733    -0.37   0.712    -2.753037    1.883532
        172  |    .195424   1.174058     0.17   0.868    -2.121529    2.512377
        173  |  -.4347525   1.174733    -0.37   0.712    -2.753037    1.883532
        174  |  -.3284162   1.181368    -0.28   0.781    -2.659796    2.002964
        175  |  -.2266588   .9145569    -0.25   0.805    -2.031498     1.57818
        176  |  -.3284162   1.181368    -0.28   0.781    -2.659796    2.002964
        177  |   .6296289   1.158866     0.54   0.588    -1.657345    2.916602
        178  |   .4335039   .9406845     0.46   0.645    -1.422897    2.289904
        180  |   1.433504   .9406845     1.52   0.129    -.4228967    3.289904
        181  |  -.7591485   .9177602    -0.83   0.409    -2.570309    1.052012
        182  |  -.2596014   .8252688    -0.31   0.753    -1.888234    1.369031
        183  |   3.577167   1.155113     3.10   0.002       1.2976    5.856734
        184  |   .8946208   .8427383     1.06   0.290    -.7684871    2.557729
        185  |  -.8339218   1.169088    -0.71   0.477    -3.141068    1.473224
        186  |  -.2214619   .9241111    -0.24   0.811    -2.045156    1.602232
        187  |  -.4228333   1.155113    -0.37   0.715      -2.7024    1.856734
        189  |  -.4228333   1.155113    -0.37   0.715      -2.7024    1.856734
        191  |  -.4228333   1.155113    -0.37   0.715      -2.7024    1.856734
        193  |  -.4228333   1.155113    -0.37   0.715      -2.7024    1.856734
        195  |  -.5536042   .9211889    -0.60   0.549    -2.371531    1.264323
        196  |   2.228025   .9161821     2.43   0.016      .419979    4.036071
        197  |  -.1964414    .817356    -0.24   0.810    -1.809459    1.416576
        198  |  -.5318083    1.16351    -0.46   0.648    -2.827945    1.764329
        199  |  -.5297748    .817356    -0.65   0.518    -2.142792    1.083242
        200  |  -.3276531   .7744424    -0.42   0.673    -1.855982    1.200676
        201  |   2.371622   .9159253     2.59   0.010      .564083    4.179162
        202  |   4.819998   1.144773     4.21   0.000     2.560838    7.079158
        203  |   4.577167   1.155113     3.96   0.000       2.2976    6.856734
        204  |  -.3358803   1.158944    -0.29   0.772    -2.623007    1.951246
        205  |  -.2357256   .7318566    -0.32   0.748    -1.680013    1.208562
        206  |    1.78307   .9260728     1.93   0.056    -.0444952    3.610635
        207  |   .8997996   .7830044     1.15   0.252     -.645426    2.445025
        210  |  -.2656836   .8152922    -0.33   0.745    -1.874628    1.343261
        211  |  -.5002209   .8258098    -0.61   0.545    -2.129921    1.129479
        212  |  -.4202005    1.15083    -0.37   0.715    -2.691314    1.850913
        213  |  -.3647074   .8317789    -0.44   0.662    -2.006187    1.276773
        214  |  -.5572034   .9452716    -0.59   0.556    -2.422657     1.30825
        215  |   .5797995    1.15083     0.50   0.615    -1.691314    2.850913
        216  |   1.294603   .9222892     1.40   0.162    -.5254958    3.114701
        217  |   1.438321   .8337677     1.73   0.086    -.2070843    3.083725
        219  |   1.428266   .8076075     1.77   0.079    -.1655134    3.022044
        221  |   .3928598   .8252791     0.48   0.635    -1.235793    2.021513
        227  |  -.4555486   1.175527    -0.39   0.699    -2.775401    1.864304
        231  |  -.3456147   .8430019    -0.41   0.682    -2.009243    1.318013
        233  |  -.1631236   .8341012    -0.20   0.845    -1.809187     1.48294
        235  |  -.3788297   .7886939    -0.48   0.632    -1.935283    1.177624
        237  |   1.824049   .9354419     1.95   0.053    -.0220053    3.670104
        239  |   5.818188   .9208423     6.32   0.000     4.000945    7.635431
        241  |  -.2030678   1.148294    -0.18   0.860    -2.469176    2.063041
        243  |  -.8767416   1.160084    -0.76   0.451    -3.166118    1.412635
        244  |  -.2849281    .903224    -0.32   0.753    -2.067402    1.497546
        245  |  -.2030678   1.148294    -0.18   0.860    -2.469176    2.063041
        247  |   2.093628   .8230276     2.54   0.012     .4694186    3.717838
        248  |  -.2840778   .9035784    -0.31   0.754    -2.067251    1.499095
        250  |  -.5034807   1.162862    -0.43   0.666     -2.79834    1.791378
        251  |  -.4269928   .8362564    -0.51   0.610    -2.077309    1.223323
        252  |  -.2171516   1.144348    -0.19   0.850    -2.475474     2.04117
        267  |  -.4219012   1.146994    -0.37   0.713    -2.685445    1.841642
        269  |  -.2030678   1.148294    -0.18   0.860    -2.469176    2.063041
        271  |  -.9295387   .9485154    -0.98   0.328    -2.801393    .9423158
        272  |  -.5706582   1.161059    -0.49   0.624    -2.861958    1.720642
        274  |  -.5034807   1.162862    -0.43   0.666     -2.79834    1.791378
        275  |  -.6082363   .8352938    -0.73   0.467    -2.256653     1.04018
        276  |  -.2849281    .903224    -0.32   0.753    -2.067402    1.497546
        278  |  -.5034807   1.162862    -0.43   0.666     -2.79834    1.791378
        279  |  -.5703385   .9286531    -0.61   0.540    -2.402996    1.262319
        280  |  -.2171516   1.144348    -0.19   0.850    -2.475474     2.04117
        283  |  -.2185325   .9078864    -0.24   0.810    -2.010207    1.573142
        284  |   .4310425   1.163089     0.37   0.711    -1.864263    2.726348
        285  |  -.5548738   1.167691    -0.48   0.635    -2.859261    1.749514
        287  |  -.4219012   1.146994    -0.37   0.713    -2.685445    1.841642
             |
      fYes_T |    .351806   .1950212     1.80   0.073    -.0330601     .736672
        mage |  -.0123831   .0109333    -1.13   0.259    -.0339594    .0091933
    mmarried |   .1274287   .1939734     0.66   0.512    -.2553695    .5102268
       makan |  -.1799835   .1776883    -1.01   0.312    -.5306437    .1706767
mselfemplo~d |  -.3200558   .1527679    -2.10   0.038    -.6215367   -.0185749
       m2q1a |   .0478316   .0442406     1.08   0.281    -.0394752    .1351385
      2.m3q1 |   -.082034   .2227532    -0.37   0.713    -.5216278    .3575598
         trt |  -.5505037   .1910707    -2.88   0.004    -.9275735   -.1734339
       _cons |   1.136786   .6497316     1.75   0.082    -.1454315    2.419004
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000

      Command: regress fdamt i.distXtrXdateFes fYes_T mage mmarried makan
                   mselfemployed m2q1a i.m3q1 trt
        _pm_1: _b[trt]
  res. var(s):  trt
   Resampling:  Permuting trt
Clust. var(s):  uniqueVendorID
     Clusters:  207
Strata var(s):  ge01
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |  -.5505037      12    1000  0.0120  0.0034  .0062155   .0208677
------------------------------------------------------------------------------
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. ritest trt _b[trt], reps($bootstrap_reps) cluster(uniqueVendorID) strata(ge0
> 1) seed(546): reg ihs_fdamt i.distXtrXdateFes fYes_T mage mmarried makan mse
> lfemployed m2q1a i.m3q1 trt
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       335
-------------+----------------------------------   F(157, 177)     =      2.22
       Model |  90.9900203       157  .579554269   Prob > F        =    0.0000
    Residual |  46.1235239       177  .260584881   R-squared       =    0.6636
-------------+----------------------------------   Adj R-squared   =    0.3652
       Total |  137.113544       334  .410519593   Root MSE        =    .51048

------------------------------------------------------------------------------
   ihs_fdamt | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.2772324   .6012827    -0.46   0.645    -1.463838    .9093733
          5  |   .9594818   .4383892     2.19   0.030     .0943396    1.824624
          6  |  -.0975332   .6206366    -0.16   0.875    -1.322333    1.127267
          7  |  -.2772324   .6012827    -0.46   0.645    -1.463838    .9093733
          8  |     -.0332   .3982219    -0.08   0.934    -.8190739    .7526738
         11  |  -.0295937   .6041056    -0.05   0.961     -1.22177    1.162583
         14  |  -.0295937   .6041056    -0.05   0.961     -1.22177    1.162583
         18  |  -.0975332   .6206366    -0.16   0.875    -1.322333    1.127267
         22  |  -.2772324   .6012827    -0.46   0.645    -1.463838    .9093733
         23  |  -.0414445   .4211535    -0.10   0.922    -.8725729    .7896838
         24  |  -.0975332   .6206366    -0.16   0.875    -1.322333    1.127267
         26  |  -.1688184   .3919592    -0.43   0.667     -.942333    .6046963
         27  |  -.0975332   .6206366    -0.16   0.875    -1.322333    1.127267
         29  |  -.1683103   .4007183    -0.42   0.675    -.9591107      .62249
         30  |  -.0975332   .6206366    -0.16   0.875    -1.322333    1.127267
         32  |   .0213526   .3741593     0.06   0.955    -.7170349      .75974
         33  |  -.0975332   .6206366    -0.16   0.875    -1.322333    1.127267
         34  |  -.2772324   .6012827    -0.46   0.645    -1.463838    .9093733
         35  |  -.0732426   .4301987    -0.17   0.865    -.9222213    .7757362
         37  |  -.1498706   .3510147    -0.43   0.670     -.842583    .5428418
         38  |    .067049    .367439     0.18   0.855     -.658076     .792174
         39  |  -.1310828    .420618    -0.31   0.756    -.9611544    .6989887
         40  |   .4310805   .3602582     1.20   0.233    -.2798736    1.142035
         41  |    -.05356    .370905    -0.14   0.885    -.7855252    .6784051
         42  |    .088055   .4071472     0.22   0.829    -.7154326    .8915426
         51  |  -.1755912   .4216327    -0.42   0.678    -1.007665    .6564828
         52  |  -.3877985   .4296877    -0.90   0.368    -1.235769    .4601717
         53  |  -.1516921   .4272888    -0.36   0.723    -.9949282     .691544
         54  |  -.1029562   .4333963    -0.24   0.812    -.9582453    .7523329
         55  |  -.1256173   .3958929    -0.32   0.751    -.9068949    .6556604
         56  |  -.0439675   .4308065    -0.10   0.919    -.8941457    .8062107
         57  |   1.024691   .4811245     2.13   0.035     .0752128     1.97417
         58  |   .5672478    .474879     1.19   0.234    -.3699056    1.504401
         61  |  -.1567834   .4305348    -0.36   0.716    -1.006425    .6928587
         62  |  -.2303827    .474401    -0.49   0.628    -1.166593    .7058275
         63  |  -.2275135   .4335882    -0.52   0.600    -1.083181    .6281543
         64  |  -.4510066   .6033722    -0.75   0.456    -1.641736    .7397227
         65  |  -.0051549   .4269455    -0.01   0.990    -.8477134    .8374037
         66  |    1.83222   .4823737     3.80   0.000     .8802761    2.784164
         67  |   1.021076   .3872064     2.64   0.009     .2569403    1.785211
         68  |  -.4510066   .6033722    -0.75   0.456    -1.641736    .7397227
         69  |   .0650917   .6035086     0.11   0.914    -1.125907     1.25609
         71  |   .2016028    .382656     0.53   0.599    -.5535524     .956758
         72  |  -.4510066   .6033722    -0.75   0.456    -1.641736    .7397227
         89  |   .0335475   .4226802     0.08   0.937    -.8005938    .8676888
         90  |   -.209211    .600784    -0.35   0.728    -1.394833    .9764105
         91  |  -.3163342   .4416983    -0.72   0.475    -1.188007    .5553384
         93  |   .0661612    .401109     0.16   0.869    -.7254102    .8577326
         94  |  -.3509756   .4871201    -0.72   0.472    -1.312286    .6103351
         95  |  -.3389449   .4110773    -0.82   0.411    -1.150188    .4722985
         97  |  -.0819781   .4004992    -0.20   0.838     -.872346    .7083899
         98  |  -.2303827    .474401    -0.49   0.628    -1.166593    .7058275
         99  |  -.2746431   .4115979    -0.67   0.505    -1.086914    .5376277
        102  |   .2455057   .4743309     0.52   0.605     -.690566    1.181577
        103  |   .9689475    .475605     2.04   0.043     .0303614    1.907534
        104  |   .2534007   .6148292     0.41   0.681    -.9599385     1.46674
        107  |  -.0127851   .4312182    -0.03   0.976    -.8637758    .8382056
        109  |  -.2889176   .4746289    -0.61   0.543    -1.225577    .6477422
        110  |  -.1415456    .401153    -0.35   0.725    -.9332039    .6501126
        113  |     -.1706   .5938194    -0.29   0.774    -1.342477    1.001277
        114  |  -.2807784   .4757963    -0.59   0.556    -1.219742    .6581852
        117  |     -.1706   .5938194    -0.29   0.774    -1.342477    1.001277
        118  |  -.3488662   .3991147    -0.87   0.383    -1.136502    .4387696
        137  |  -.3475663   .6019498    -0.58   0.564    -1.535489     .840356
        138  |  -.2571233    .429353    -0.60   0.550    -1.104433    .5901864
        141  |  -.4072352   .6043473    -0.67   0.501    -1.599889    .7854183
        142  |  -.3080416   .6014935    -0.51   0.609    -1.495063    .8789801
        145  |   -.230269   .5954963    -0.39   0.699    -1.405455    .9449175
        146  |  -.2822108   .4309106    -0.65   0.513    -1.132594    .5681728
        149  |   .5213832   .4719631     1.10   0.271    -.4100158    1.452782
        150  |   .6373213   .4244512     1.50   0.135     -.200315    1.474958
        153  |     -.1706   .5938194    -0.29   0.774    -1.342477    1.001277
        154  |  -.0131289   .6010149    -0.02   0.983    -1.199206    1.172948
        157  |  -.1203361   .4747428    -0.25   0.800    -1.057221    .8165485
        158  |  -.0355273   .4883055    -0.07   0.942    -.9991773    .9281228
        159  |  -.0051782   .5940106    -0.01   0.993    -1.177433    1.167076
        160  |   1.895781   .6132436     3.09   0.002     .6855706     3.10599
        162  |   .6824416   .6132436     1.11   0.267    -.5277683    1.892652
        171  |  -.2354941   .6097989    -0.39   0.700    -1.438906    .9679179
        172  |   .1278774   .6094488     0.21   0.834    -1.074844    1.330598
        173  |  -.2354941   .6097989    -0.39   0.700    -1.438906    .9679179
        174  |   -.198932   .6132436    -0.32   0.746    -1.409142    1.011278
        175  |  -.1203361   .4747428    -0.25   0.800    -1.057221    .8165485
        176  |   -.198932   .6132436    -0.32   0.746    -1.409142    1.011278
        177  |   .6992292   .6015629     1.16   0.247    -.4879295    1.886388
        178  |   .4051595   .4883055     0.83   0.408    -.5584905     1.36881
        180  |    .873696   .4883055     1.79   0.075    -.0899541    1.837346
        181  |  -.4159395   .4764056    -0.87   0.384    -1.356106    .5242266
        182  |  -.1258373   .4283937    -0.29   0.769     -.971254    .7195794
        183  |     1.8369   .5996147     3.06   0.003      .653586    3.020214
        184  |   .4797813    .437462     1.10   0.274    -.3835313    1.343094
        185  |  -.4335982    .606869    -0.71   0.476    -1.631228    .7640319
        186  |  -.0959691   .4797024    -0.20   0.842    -1.042641    .8507029
        187  |  -.2578125   .5996147    -0.43   0.668    -1.441126    .9255015
        189  |  -.2578125   .5996147    -0.43   0.668    -1.441126    .9255015
        191  |  -.2578125   .5996147    -0.43   0.668    -1.441126    .9255015
        193  |  -.2578125   .5996147    -0.43   0.668    -1.441126    .9255015
        195  |  -.3280466   .4781854    -0.69   0.494    -1.271725    .6156319
        196  |   1.020515   .4755864     2.15   0.033     .0819656    1.959065
        197  |  -.0104505   .4242862    -0.02   0.980    -.8477612    .8268602
        198  |   -.276508   .6039733    -0.46   0.648    -1.468423    .9154075
        199  |  -.3042417   .4242862    -0.72   0.474    -1.141552     .533069
        200  |   -.163505   .4020099    -0.41   0.685    -.9568543    .6298444
        201  |   1.472741   .4754532     3.10   0.002     .5344547    2.411028
        202  |   2.212897   .5942469     3.72   0.000     1.040176    3.385617
        203  |   2.054626   .5996147     3.43   0.001     .8713118     3.23794
        204  |  -.1855736   .6016032    -0.31   0.758    -1.372812    1.001665
        205  |  -.1214312   .3799038    -0.32   0.750    -.8711551    .6282927
        206  |    .945129   .4807207     1.97   0.051    -.0035526    1.893811
        207  |   .4124434   .4064544     1.01   0.312     -.389677    1.214564
        210  |  -.1380986   .4232149    -0.33   0.745    -.9732952    .6970979
        211  |  -.2560761   .4286745    -0.60   0.551    -1.102047    .5898947
        212  |  -.2219166   .5973911    -0.37   0.711    -1.400842    .9570092
        213  |  -.1762833   .4317731    -0.41   0.684    -1.028369    .6758024
        214  |  -.2729551   .4906867    -0.56   0.579    -1.241304    .6953941
        215  |    .659457   .5973911     1.10   0.271    -.5194688    1.838383
        216  |   .8132344   .4787566     1.70   0.091    -.1315713     1.75804
        217  |   .6682184   .4328055     1.54   0.124    -.1859047    1.522341
        219  |   .7593179   .4192258     1.81   0.072    -.0680063    1.586642
        221  |   .4437621   .4283991     1.04   0.302    -.4016651    1.289189
        227  |  -.2109243   .6102115    -0.35   0.730    -1.415151    .9933019
        231  |  -.1615831   .4375989    -0.37   0.712    -1.025166    .7019996
        233  |  -.0797882   .4329786    -0.18   0.854    -.9342529    .7746765
        235  |  -.1855833   .4094078    -0.45   0.651     -.993532    .6223655
        237  |   1.259725   .4855841     2.59   0.010     .3014455    2.218005
        239  |   2.390048   .4780055     5.00   0.000     1.446724    3.333371
        241  |  -.1120185   .5960746    -0.19   0.851    -1.288346    1.064309
        243  |  -.4942926   .6021951    -0.82   0.413    -1.682699    .6941136
        244  |  -.1531332   .4688599    -0.33   0.744    -1.078408    .7721418
        245  |  -.1120185   .5960746    -0.19   0.851    -1.288346    1.064309
        247  |   1.075417   .4272303     2.52   0.013     .2322967    1.918538
        248  |  -.1524285   .4690439    -0.32   0.746    -1.078067    .7732095
        250  |  -.2676994   .6036371    -0.44   0.658    -1.458951    .9235527
        251  |  -.1230778   .4340974    -0.28   0.777    -.9797503    .7335947
        252  |  -.1203709   .5940265    -0.20   0.840    -1.292657    1.051915
        267  |  -.2233259      .5954    -0.38   0.708    -1.398322    .9516705
        269  |  -.1120185   .5960746    -0.19   0.851    -1.288346    1.064309
        271  |  -.5136405   .4923705    -1.04   0.298    -1.485313    .4580316
        272  |  -.2987465   .6027009    -0.50   0.621    -1.488151     .890658
        274  |  -.2676994   .6036371    -0.44   0.658    -1.458951    .9235527
        275  |  -.3339561   .4335977    -0.77   0.442    -1.189643    .5217303
        276  |  -.1531332   .4688599    -0.33   0.744    -1.078408    .7721418
        278  |  -.2676994   .6036371    -0.44   0.658    -1.458951    .9235527
        279  |  -.2898089   .4820601    -0.60   0.548    -1.241134     .661516
        280  |  -.1203709   .5940265    -0.20   0.840    -1.292657    1.051915
        283  |  -.1128427   .4712802    -0.24   0.811    -1.042894    .8172085
        284  |   .5840364   .6037547     0.97   0.335    -.6074477     1.77552
        285  |  -.2889848   .6061435    -0.48   0.634    -1.485183    .9072134
        287  |  -.2233259      .5954    -0.38   0.708    -1.398322    .9516705
             |
      fYes_T |   .1769663   .1012347     1.75   0.082    -.0228162    .3767487
        mage |  -.0069431   .0056754    -1.22   0.223    -.0181433    .0042571
    mmarried |   .0538371   .1006908     0.53   0.594    -.1448718    .2525461
       makan |  -.0890688   .0922373    -0.97   0.336    -.2710951    .0929575
mselfemplo~d |  -.1723649   .0793012    -2.17   0.031    -.3288624   -.0158674
       m2q1a |   .0263629   .0229651     1.15   0.253    -.0189577    .0716836
      2.m3q1 |  -.0519374   .1156303    -0.45   0.654    -.2801288    .1762539
         trt |  -.3236926    .099184    -3.26   0.001    -.5194281   -.1279572
       _cons |   .6400971   .3372731     1.90   0.059    -.0254968    1.305691
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000

      Command: regress ihs_fdamt i.distXtrXdateFes fYes_T mage mmarried
                   makan mselfemployed m2q1a i.m3q1 trt
        _pm_1: _b[trt]
  res. var(s):  trt
   Resampling:  Permuting trt
Clust. var(s):  uniqueVendorID
     Clusters:  207
Strata var(s):  ge01
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |  -.3236926       6    1000  0.0060  0.0024   .002205   .0130134
------------------------------------------------------------------------------
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. *mht: implement Romano-Wolf (2005) procedure, pval: *[allows for arbitrary d
> ependence and corrects for familywise error rate (FWER) (see: Clarke, Romano
> , and Wolf (2020))]**
. rwolf fd fdamt ihs_fdamt, indepvar(trt trt2 trt3 trt4) reps($bootstrap_reps)
>  seed(124) controls(i.distXtrXdateFes fYes_T mage mmarried makan mselfemploy
> ed m2q1a i.m3q1) //family (misconduct: 0/1, amount)
Bootstrap replications (1000). This may take some time.
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..................................................     50
..................................................     100
..................................................     150
..................................................     200
..................................................     250
..................................................     300
..................................................     350
..................................................     400
..................................................     450
..................................................     500
..................................................     550
..................................................     600
..................................................     650
..................................................     700
..................................................     750
..................................................     800
..................................................     850
..................................................     900
..................................................     950
..................................................     1000




Romano-Wolf step-down adjusted p-values


Independent variable:  trt
Outcome variables:   fd fdamt ihs_fdamt
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
                 fd |     0.0009             0.0380              0.0440
              fdamt |     0.0094             0.0619              0.0619
          ihs_fdamt |     0.0034             0.0400              0.0480
------------------------------------------------------------------------------


Independent variable:  trt2
Outcome variables:   fd fdamt ihs_fdamt
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
                 fd |     0.6854             0.7143              0.7842
              fdamt |     0.6041             0.6124              0.7842
          ihs_fdamt |     0.5068             0.5415              0.6993
------------------------------------------------------------------------------


Independent variable:  trt3
Outcome variables:   fd fdamt ihs_fdamt
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
                 fd |     0.9169             0.9191              0.9890
              fdamt |     0.9126             0.9351              0.9890
          ihs_fdamt |     0.8670             0.8971              0.9770
------------------------------------------------------------------------------


Independent variable:  trt4
Outcome variables:   fd fdamt ihs_fdamt
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
                 fd |          .             0.0010              0.0010
              fdamt |          .             0.0010              0.0010
          ihs_fdamt |          .             0.0010              0.0010
------------------------------------------------------------------------------



. *attrition bounds
. leebounds fd trt, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   2319
Number of selected obs.            =   1155
Trimming porportion                =   0.0098
Effect 95% conf. interval          : [-0.2357  -0.0805]

------------------------------------------------------------------------------
          fd | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt          |
       lower |   -.174656   .0327728    -5.33   0.000    -.2388894   -.1104225
       upper |  -.1647908   .0452559    -3.64   0.000    -.2534906   -.0760909
------------------------------------------------------------------------------

. leebounds fdamt trt, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   2319
Number of selected obs.            =   1155
Trimming porportion                =   0.0098
Effect 95% conf. interval          : [-0.6752  -0.0138]

------------------------------------------------------------------------------
       fdamt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt          |
       lower |  -.4849077   .1021503    -4.75   0.000    -.6851186   -.2846968
       upper |  -.4355816   .2264276    -1.92   0.054    -.8793715    .0082083
------------------------------------------------------------------------------

. leebounds ihs_fdamt trt, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   2319
Number of selected obs.            =   1155
Trimming porportion                =   0.0098
Effect 95% conf. interval          : [-0.3862  -0.0769]

------------------------------------------------------------------------------
   ihs_fdamt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt          |
       lower |   -.280828   .0543562    -5.17   0.000    -.3873641   -.1742918
       upper |  -.2763505   .1028956    -2.69   0.007    -.4780221   -.0746789
------------------------------------------------------------------------------

. 
. ** Table C.2 ---------------------------------------------------------------
> ----
. *SEPARATE*
. *wild cluster bootstrap, pval
. reg fd i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a i.m3
> q1 trt2 trt3 trt4, r cluster(uniqueVendorID) level(95)

Linear regression                               Number of obs     =        335
                                                F(75, 90)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6876
                                                Root MSE          =     .29258

                        (Std. err. adjusted for 91 clusters in uniqueVendorID)
------------------------------------------------------------------------------
             |               Robust
          fd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.1720881   .1223245    -1.41   0.163    -.4151071    .0709308
          5  |   .4065839   .3831034     1.06   0.291    -.3545178    1.167686
          6  |  -.0451877   .1625648    -0.28   0.782    -.3681511    .2777757
          7  |  -.1720881   .1223245    -1.41   0.163    -.4151071    .0709308
          8  |   .0127877   .0788603     0.16   0.872     -.143882    .1694574
         11  |   .0228261   .0979286     0.23   0.816    -.1717261    .2173784
         14  |   .0228261   .0979286     0.23   0.816    -.1717261    .2173784
         18  |  -.0451877   .1625648    -0.28   0.782    -.3681511    .2777757
         22  |  -.1720881   .1223245    -1.41   0.163    -.4151071    .0709308
         23  |   .0008988   .0850567     0.01   0.992    -.1680812    .1698789
         24  |  -.0451877   .1625648    -0.28   0.782    -.3681511    .2777757
         26  |  -.0747341   .1393126    -0.54   0.593     -.351503    .2020347
         27  |  -.0451877   .1625648    -0.28   0.782    -.3681511    .2777757
         29  |  -.0861459   .1393614    -0.62   0.538    -.3630117      .19072
         30  |  -.0451877   .1625648    -0.28   0.782    -.3681511    .2777757
         32  |   .1149532   .2083837     0.55   0.583    -.2990374    .5289439
         33  |  -.0451877   .1625648    -0.28   0.782    -.3681511    .2777757
         34  |  -.1720881   .1223245    -1.41   0.163    -.4151071    .0709308
         35  |     -.0185    .070421    -0.26   0.793    -.1584036    .1214037
         37  |  -.0795245   .0863999    -0.92   0.360    -.2511729    .0921239
         38  |  -.0011219   .1592164    -0.01   0.994     -.317433    .3151893
         39  |  -.0795102   .0856801    -0.93   0.356    -.2497287    .0907084
         40  |   .2021132   .2231855     0.91   0.368    -.2412838    .6455103
         41  |   .0598904   .2101378     0.29   0.776    -.3575849    .4773658
         42  |   .3120794   .2709937     1.15   0.253    -.2262968    .8504557
         51  |  -.0926029   .0809984    -1.14   0.256    -.2535204    .0683146
         52  |  -.2089177   .1283851    -1.63   0.107     -.463977    .0461416
         53  |  -.0869915   .0923629    -0.94   0.349    -.2704864    .0965034
         54  |  -.0301392   .1443942    -0.21   0.835    -.3170034     .256725
         55  |  -.0745278   .0976788    -0.76   0.447    -.2685837    .1195281
         56  |  -.0008763   .1221959    -0.01   0.994    -.2436398    .2418873
         57  |   .8308642    .095306     8.72   0.000     .6415223    1.020206
         58  |   .8300699   .1762097     4.71   0.000     .4799986    1.180141
         61  |  -.0652686   .1076721    -0.61   0.546     -.279178    .1486408
         62  |  -.1204084   .0920557    -1.31   0.194    -.3032931    .0624763
         63  |  -.1133814    .130315    -0.87   0.387    -.3722749    .1455121
         64  |  -.2580322   .1173827    -2.20   0.030    -.4912335   -.0248309
         65  |   .0158098   .1441114     0.11   0.913    -.2704928    .3021123
         66  |   .8411558   .1263273     6.66   0.000     .5901846    1.092127
         67  |   .4761427   .2323769     2.05   0.043     .0144854    .9377999
         68  |  -.2580322   .1173827    -2.20   0.030    -.4912335   -.0248309
         69  |   .0717065   .1226236     0.58   0.560    -.1719067    .3153198
         71  |   .3186749   .2533904     1.26   0.212    -.1847293    .8220792
         72  |  -.2580322   .1173827    -2.20   0.030    -.4912335   -.0248309
         89  |   .0372121   .1141837     0.33   0.745    -.1896337    .2640579
         90  |  -.1370349   .1184461    -1.16   0.250    -.3723487    .0982789
         91  |  -.1735912   .1655894    -1.05   0.297    -.5025635    .1553811
         93  |   .0619154   .0976845     0.63   0.528     -.132152    .2559828
         94  |  -.2027385   .1396945    -1.45   0.150    -.4802661     .074789
         95  |  -.1738935   .1425674    -1.22   0.226    -.4571285    .1093416
         97  |  -.0204617   .0947299    -0.22   0.829    -.2086593    .1677358
         98  |  -.1204084   .0920557    -1.31   0.194    -.3032931    .0624763
         99  |  -.1407087   .1582444    -0.89   0.376    -.4550887    .1736714
        102  |   .4016768   .5797787     0.69   0.490    -.7501549    1.553508
        103  |   1.057077   .0883545    11.96   0.000     .8815455    1.232609
        104  |    .654179   .1403923     4.66   0.000     .3752651    .9330929
        107  |   .0132359    .097224     0.14   0.892    -.1799166    .2063884
        109  |  -.1638366   .1164253    -1.41   0.163    -.3951358    .0674627
        110  |  -.0760628    .099869    -0.76   0.448    -.2744699    .1223444
        113  |  -.0923236   .0927036    -1.00   0.322    -.2764955    .0918483
        114  |  -.1795011   .1792709    -1.00   0.319    -.5356539    .1766518
        117  |  -.0923236   .0927036    -1.00   0.322    -.2764955    .0918483
        118  |   -.205686   .1056661    -1.95   0.055    -.4156102    .0042382
        137  |  -.1801123   .1046399    -1.72   0.089    -.3879978    .0277731
        138  |  -.1530189   .1082637    -1.41   0.161    -.3681035    .0620658
        141  |  -.2353496   .1110706    -2.12   0.037    -.4560107   -.0146885
        142  |  -.2132201   .1141509    -1.87   0.065    -.4400007    .0135605
        145  |  -.1475608    .102312    -1.44   0.153    -.3508214    .0556997
        146  |  -.1599085   .1085612    -1.47   0.144    -.3755842    .0557672
        149  |   .3800578    .472897     0.80   0.424    -.5594347     1.31955
        150  |   .8495581   .1388102     6.12   0.000     .5737874    1.125329
        153  |  -.0923236   .0927036    -1.00   0.322    -.2764955    .0918483
        154  |  -.0275626   .1236176    -0.22   0.824    -.2731506    .2180254
        157  |  -.0870981   .1225964    -0.71   0.479    -.3306572    .1564611
        158  |  -.0042501   .1722901    -0.02   0.980    -.3465344    .3380343
        159  |  -.0129404   .0861864    -0.15   0.881    -.1841648     .158284
        160  |   .8828688   .1494761     5.91   0.000     .5859085    1.179829
        162  |   .8828688   .1494761     5.91   0.000     .5859085    1.179829
        171  |  -.1612557   .1487806    -1.08   0.281    -.4568344     .134323
        172  |   .1086311   .1328292     0.82   0.416    -.1552573    .3725195
        173  |  -.1612557   .1487806    -1.08   0.281    -.4568344     .134323
        174  |  -.1171312   .1494761    -0.78   0.435    -.4140915     .179829
        175  |  -.0870981   .1225964    -0.71   0.479    -.3306572    .1564611
        176  |  -.1171312   .1494761    -0.78   0.435    -.4140915     .179829
        177  |   .8992708   .1002585     8.97   0.000     .7000899    1.098452
        178  |   .4957499   .4024777     1.23   0.221    -.3038423    1.295342
        180  |   .4957499   .4024777     1.23   0.221    -.3038423    1.295342
        181  |  -.2271411   .1330453    -1.71   0.091    -.4914589    .0371767
        182  |  -.0425617   .1042074    -0.41   0.684    -.2495879    .1644644
        183  |   .8364833   .1161987     7.20   0.000     .6056344    1.067332
        184  |   .2343429   .4003746     0.59   0.560    -.5610712    1.029757
        185  |  -.2209592   .1185688    -1.86   0.066    -.4565167    .0145984
        186  |  -.0306343   .1209722    -0.25   0.801    -.2709666    .2096981
        187  |  -.1635167   .1161987    -1.41   0.163    -.3943656    .0673323
        189  |  -.1635167   .1161987    -1.41   0.163    -.3943656    .0673323
        191  |  -.1635167   .1161987    -1.41   0.163    -.3943656    .0673323
        193  |  -.1635167   .1161987    -1.41   0.163    -.3943656    .0673323
        195  |  -.1984199   .1134156    -1.75   0.084    -.4237398    .0269001
        196  |   .4560307   .4320452     1.06   0.294    -.4023025    1.314364
        197  |   .1566633   .4195311     0.37   0.710    -.6768085    .9901351
        198  |  -.1133586   .1019441    -1.11   0.269    -.3158883    .0891711
        199  |    -.17667   .0987305    -1.79   0.077    -.3728153    .0194752
        200  |  -.0602609   .0966143    -0.62   0.534     -.252202    .1316801
        201  |   .8077621    .102283     7.90   0.000      .604559    1.010965
        202  |   .9744302   .0796582    12.23   0.000     .8161754    1.132685
        203  |   .8364833   .1161987     7.20   0.000     .6056344    1.067332
        204  |  -.0664166   .1035496    -0.64   0.523    -.2721359    .1393027
        205  |  -.0698484   .0992254    -0.70   0.483     -.266977    .1272801
        206  |   .4556583   .4253978     1.07   0.287    -.3894685    1.300785
        207  |   .1733344   .2714468     0.64   0.525     -.365942    .7126108
        210  |  -.0811929   .1011052    -0.80   0.424    -.2820559    .1196702
        211  |  -.1397187   .1042391    -1.34   0.183    -.3468079    .0673704
        212  |  -.1142549   .1003154    -1.14   0.258    -.3135488    .0850391
        213  |  -.0931099   .1315138    -0.71   0.481    -.3543851    .1681653
        214  |  -.1431088   .1426948    -1.00   0.319    -.4265969    .1403794
        215  |   .8857451   .1003154     8.83   0.000     .6864512    1.085039
        216  |     .44468   .4194746     1.06   0.292    -.3886795     1.27804
        217  |   .3062412    .360535     0.85   0.398    -.4100246    1.022507
        219  |   .3561232   .3965357     0.90   0.372    -.4316641    1.143911
        221  |   .5878637   .3483942     1.69   0.095    -.1042822    1.280009
        227  |  -.0942547   .1226639    -0.77   0.444     -.337948    .1494387
        231  |  -.0563551   .1108997    -0.51   0.613    -.2766766    .1639665
        233  |   -.020136   .1065086    -0.19   0.850     -.231734     .191462
        235  |  -.0765606   .1049232    -0.73   0.467    -.2850089    .1318876
        237  |   .9711822   .1264186     7.68   0.000     .7200295    1.222335
        239  |    .960074   .1095022     8.77   0.000     .7425287    1.177619
        241  |  -.0360592   .0877854    -0.41   0.682    -.2104603    .1383419
        243  |  -.3039526   .1198728    -2.54   0.013    -.5421007   -.0658044
        244  |  -.0831659   .0890589    -0.93   0.353    -.2600969    .0937651
        245  |  -.0360592   .0877854    -0.41   0.682    -.2104603    .1383419
        247  |   .4685145   .2855657     1.64   0.104    -.0988116    1.035841
        248  |      -.098   .0903276    -1.08   0.281    -.2774515    .0814516
        250  |  -.1351809   .1164517    -1.16   0.249    -.3665325    .0961707
        251  |   .0907317   .3043854     0.30   0.766    -.5139829    .6954464
        252  |  -.0693651   .0847938    -0.82   0.415    -.2378229    .0990926
        267  |  -.1170327   .0952595    -1.23   0.222    -.3062824     .072217
        269  |  -.0360592   .0877854    -0.41   0.682    -.2104603    .1383419
        271  |  -.3053861   .1657339    -1.84   0.069    -.6346454    .0238733
        272  |  -.1274858   .1027841    -1.24   0.218    -.3316843    .0767128
        274  |  -.1351809   .1164517    -1.16   0.249    -.3665325    .0961707
        275  |  -.1991078    .160905    -1.24   0.219    -.5187737    .1205581
        276  |  -.0831659   .0890589    -0.93   0.353    -.2600969    .0937651
        278  |  -.1351809   .1164517    -1.16   0.249    -.3665325    .0961707
        279  |   -.147147   .1194219    -1.23   0.221    -.3843993    .0901054
        280  |  -.0693651   .0847938    -0.82   0.415    -.2378229    .0990926
        283  |  -.0593582   .1018445    -0.58   0.561      -.26169    .1429736
        284  |   .8428461   .0988696     8.52   0.000     .6464243    1.039268
        285  |   -.123848   .1089731    -1.14   0.259     -.340342    .0926461
        287  |  -.1170327   .0952595    -1.23   0.222    -.3062824     .072217
             |
      fYes_T |   .0877888   .0550661     1.59   0.114    -.0216097    .1971872
        mage |  -.0037831   .0033504    -1.13   0.262    -.0104393    .0028731
    mmarried |   .0189093   .0730775     0.26   0.796    -.1262719    .1640906
       makan |  -.0668464   .0642069    -1.04   0.301    -.1944047    .0607119
mselfemplo~d |  -.0831663   .0508262    -1.64   0.105    -.1841414    .0178089
       m2q1a |   .0123546   .0170696     0.72   0.471    -.0215572    .0462663
      2.m3q1 |  -.0426023   .0753184    -0.57   0.573    -.1922355    .1070309
        trt2 |   -.184884    .094593    -1.95   0.054    -.3728094    .0030415
        trt3 |    -.21733   .0938898    -2.31   0.023    -.4038585   -.0308015
        trt4 |   -.211629   .0898374    -2.36   0.021    -.3901067   -.0331513
       _cons |   .3833977   .1257925     3.05   0.003     .1334889    .6333065
------------------------------------------------------------------------------

. boottest trt2, rep($bootstrap_reps) level(95) nogr

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueVendorID, Rademacher weights:
  trt2

                           t(90) =    -1.9545
                        Prob>|t| =     0.0380

95% confidence set for null hypothesis expression: [−.3721, −.008794]

. boottest trt3, rep($bootstrap_reps) level(95) nogr

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueVendorID, Rademacher weights:
  trt3

                           t(90) =    -2.3147
                        Prob>|t| =     0.0150

95% confidence set for null hypothesis expression: [−.4131, −.0256]

. boottest trt4, rep($bootstrap_reps) level(95) nogr

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueVendorID, Rademacher weights:
  trt4

                           t(90) =    -2.3557
                        Prob>|t| =     0.0170

95% confidence set for null hypothesis expression: [−.3932, −.03628]

. reg fdamt i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a i
> .m3q1 trt2 trt3 trt4, r cluster(uniqueVendorID) level(95)

Linear regression                               Number of obs     =        335
                                                F(76, 90)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6634
                                                Root MSE          =     .98782

                        (Std. err. adjusted for 91 clusters in uniqueVendorID)
------------------------------------------------------------------------------
             |               Robust
       fdamt | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.4269911   .3471543    -1.23   0.222    -1.116674    .2626915
          5  |   1.928633   1.577254     1.22   0.225    -1.204857    5.062124
          6  |  -.2788827   .4899852    -0.57   0.571    -1.252324    .6945584
          7  |  -.4269911   .3471543    -1.23   0.222    -1.116674    .2626915
          8  |  -.0189659   .2613268    -0.07   0.942    -.5381373    .5002054
         11  |   .0040794    .316513     0.01   0.990    -.6247289    .6328876
         14  |   .0040794    .316513     0.01   0.990    -.6247289    .6328876
         18  |  -.2788827   .4899852    -0.57   0.571    -1.252324    .6945584
         22  |  -.4269911   .3471543    -1.23   0.222    -1.116674    .2626915
         23  |  -.0335199   .2849248    -0.12   0.907    -.5995728     .532533
         24  |  -.2788827   .4899852    -0.57   0.571    -1.252324    .6945584
         26  |  -.2597124   .4031631    -0.64   0.521    -1.060666    .5412416
         27  |  -.2788827   .4899852    -0.57   0.571    -1.252324    .6945584
         29  |  -.2608157    .400951    -0.65   0.517    -1.057375    .5357435
         30  |  -.2788827   .4899852    -0.57   0.571    -1.252324    .6945584
         32  |  -.0118559   .3763098    -0.03   0.975     -.759461    .7357492
         33  |  -.2788827   .4899852    -0.57   0.571    -1.252324    .6945584
         34  |  -.4269911   .3471543    -1.23   0.222    -1.116674    .2626915
         35  |   -.142026   .2497638    -0.57   0.571    -.6382254    .3541735
         37  |  -.2998468   .2931662    -1.02   0.309    -.8822727    .2825792
         38  |   .2408533   .7456581     0.32   0.747    -1.240527    1.722233
         39  |  -.2844996     .28785    -0.99   0.326    -.8563639    .2873647
         40  |   .8709117    .881873     0.99   0.326    -.8810829    2.622906
         41  |  -.2288246   .3426407    -0.67   0.506    -.9095403     .451891
         42  |  -.1177637   .4299638    -0.27   0.785    -.9719619    .7364345
         51  |  -.3276455    .281603    -1.16   0.248    -.8870989     .231808
         52  |   -.685703   .4374666    -1.57   0.121    -1.554807    .1834007
         53  |  -.3605983   .3012748    -1.20   0.234    -.9591333    .2379367
         54  |  -.1727535    .482066    -0.36   0.721    -1.130462    .7849547
         55  |   -.258522     .32386    -0.80   0.427    -.9019264    .3848825
         56  |  -.0573906   .3839516    -0.15   0.882    -.8201775    .7053962
         57  |   1.353872   1.023357     1.32   0.189    -.6792056    3.386949
         58  |    .465996   .5547713     0.84   0.403     -.636154    1.568146
         61  |  -.2705503   .3654301    -0.74   0.461    -.9965409    .4554404
         62  |  -.3645668   .2986433    -1.22   0.225    -.9578738    .2287403
         63  |  -.4159627    .410163    -1.01   0.313    -1.230823    .3988977
         64  |  -.7425862    .394333    -1.88   0.063    -1.525997     .040825
         65  |   .0023549   .4809864     0.00   0.996    -.9532085    .9579183
         66  |   3.579952    .398136     8.99   0.000     2.788985    4.370919
         67  |    2.00912   1.002058     2.00   0.048      .018358    3.999883
         68  |  -.7425862    .394333    -1.88   0.063    -1.525997     .040825
         69  |   .1800416   .3810966     0.47   0.638    -.5770734    .9371566
         71  |   .1152452   .3287538     0.35   0.727    -.5378816    .7683719
         72  |  -.7425862    .394333    -1.88   0.063    -1.525997     .040825
         89  |   .1020436   .3722135     0.27   0.785    -.6374236    .8415107
         90  |  -.3083842   .3405723    -0.91   0.368    -.9849906    .3682222
         91  |  -.5881867   .5116931    -1.15   0.253    -1.604754    .4283809
         93  |   .1550407   .3253991     0.48   0.635    -.4914213    .8015027
         94  |  -.5930923   .4205545    -1.41   0.162    -1.428597    .2424126
         95  |  -.5938275   .4446259    -1.34   0.185    -1.477154    .2894994
         97  |   -.129269   .3030459    -0.43   0.671    -.7313226    .4727846
         98  |  -.3645668   .2986433    -1.22   0.225    -.9578738    .2287403
         99  |  -.4719364   .4721622    -1.00   0.320    -1.409969    .4660961
        102  |   .1968137    .789202     0.25   0.804    -1.371074    1.764701
        103  |   1.135299   .2929912     3.87   0.000     .5532214    1.717378
        104  |  -.0886749   .4604395    -0.19   0.848    -1.003418    .8260685
        107  |   -.034553   .3436316    -0.10   0.920    -.7172371    .6481311
        109  |  -.5813866   .4228803    -1.37   0.173    -1.421512    .2587389
        110  |  -.2711185    .329638    -0.82   0.413    -.9260019    .3837648
        113  |  -.2979966   .3229888    -0.92   0.359    -.9396703    .3436771
        114  |  -.5561836   .5280782    -1.05   0.295    -1.605303    .4929361
        117  |  -.2979966   .3229888    -0.92   0.359    -.9396703    .3436771
        118  |  -.6048832   .3592382    -1.68   0.096    -1.318573    .1088061
        137  |  -.6440853   .3537228    -1.82   0.072    -1.346817    .0586469
        138  |   -.495564   .3510006    -1.41   0.161    -1.192888      .20176
        141  |  -.8647766   .3730828    -2.32   0.023    -1.605971   -.1235825
        142  |  -.5144767   .3672367    -1.40   0.165    -1.244056    .2151031
        145  |   -.518688   .3318634    -1.56   0.122    -1.177993    .1406167
        146  |   -.488688    .332391    -1.47   0.145    -1.149041    .1716648
        149  |   .5916577   .9276369     0.64   0.525    -1.251255     2.43457
        150  |   .5298788   .4268099     1.24   0.218    -.3180535    1.377811
        153  |  -.2979966   .3229888    -0.92   0.359    -.9396703    .3436771
        154  |  -.1651858   .4177026    -0.40   0.693    -.9950249    .6646532
        157  |  -.2114699   .4033696    -0.52   0.601    -1.012834    .5898941
        158  |   -.028726   .4573281    -0.06   0.950    -.9372881    .8798361
        159  |   .0040907    .304114     0.01   0.989    -.6000848    .6082662
        160  |   3.710304   .4081003     9.09   0.000     2.899541    4.521066
        162  |   .7103037   .4081003     1.74   0.085    -.1004588    1.521066
        171  |  -.4270305   .5286704    -0.81   0.421    -1.477326    .6232654
        172  |   .2322442   .3947787     0.59   0.558    -.5520526    1.016541
        173  |  -.4270305   .5286704    -0.81   0.421    -1.477326    .6232654
        174  |  -.2896963   .4081003    -0.71   0.480    -1.100459    .5210663
        175  |  -.2114699   .4033696    -0.52   0.601    -1.012834    .5898941
        176  |  -.2896963   .4081003    -0.71   0.480    -1.100459    .5210663
        177  |    .658002    .345744     1.90   0.060    -.0288788    1.344883
        178  |    .471274   .4456051     1.06   0.293    -.4139984    1.356546
        180  |   1.471274   1.274648     1.15   0.251    -1.061036    4.003584
        181  |  -.7226131   .4313674    -1.68   0.097      -1.5796    .1343735
        182  |  -.2129711   .3587092    -0.59   0.554    -.9256096    .4996674
        183  |   3.607543   .3352081    10.76   0.000     2.941593    4.273492
        184  |   .9070283   1.579562     0.57   0.567    -2.231048    4.045104
        185  |  -.7957286   .3930247    -2.02   0.046    -1.576541   -.0149165
        186  |  -.1837812   .4248366    -0.43   0.666    -1.027793    .6602308
        187  |  -.3924572   .3352081    -1.17   0.245    -1.058407    .2734921
        189  |  -.3924572   .3352081    -1.17   0.245    -1.058407    .2734921
        191  |  -.3924572   .3352081    -1.17   0.245    -1.058407    .2734921
        193  |  -.3924572   .3352081    -1.17   0.245    -1.058407    .2734921
        195  |  -.5209774   .3608809    -1.44   0.152     -1.23793    .1959754
        196  |   2.277048   2.235634     1.02   0.311    -2.164429    6.718524
        197  |  -.1638649   .5189425    -0.32   0.753    -1.194835     .867105
        198  |  -.4657963   .3461159    -1.35   0.182    -1.153416    .2218233
        199  |  -.4971982   .3180306    -1.56   0.121    -1.129021     .134625
        200  |  -.2761774   .3398614    -0.81   0.419    -.9513715    .3990167
        201  |   2.405907   .3728694     6.45   0.000     1.665137    3.146677
        202  |   4.880292   .2861066    17.06   0.000     4.311892    5.448693
        203  |   4.607543   .3352081    13.75   0.000     3.941593    5.273492
        204  |  -.2713509   .3573643    -0.76   0.450    -.9813175    .4386156
        205  |   -.226848   .3430727    -0.66   0.510    -.9084218    .4547257
        206  |   1.816047   1.738427     1.04   0.299    -1.637641    5.269735
        207  |   .9123095   1.395716     0.65   0.515    -1.860523    3.685142
        210  |  -.2701289   .3531061    -0.77   0.446    -.9716358    .4313779
        211  |  -.5008547   .3677665    -1.36   0.177    -1.231487    .2297776
        212  |  -.3895878    .335429    -1.16   0.249    -1.055976    .2768005
        213  |   -.363765   .4656685    -0.78   0.437    -1.288897    .5613668
        214  |  -.5765764   .4856314    -1.19   0.238    -1.541368    .3882153
        215  |   .6104122    .335429     1.82   0.072    -.0559761      1.2768
        216  |   1.269512   1.237813     1.03   0.308    -1.189619    3.728643
        217  |   1.481846   1.804029     0.82   0.414    -2.102172    5.065864
        219  |   1.440542   1.541195     0.93   0.352    -1.621311    4.502394
        221  |   .3816091   .4409473     0.87   0.389    -.4944097    1.257628
        227  |  -.4114752   .4512801    -0.91   0.364    -1.308022    .4850715
        231  |  -.3001836   .4050711    -0.74   0.461    -1.104928     .504561
        233  |  -.1588694   .3539463    -0.45   0.655    -.8620456    .5443068
        235  |  -.3618312   .3588392    -1.01   0.316    -1.074728    .3510655
        237  |   1.814213    .926034     1.96   0.053    -.0255155    3.653941
        239  |   5.800496   1.165346     4.98   0.000     3.485332    8.115659
        241  |  -.1624693   .3015308    -0.54   0.591    -.7615129    .4365744
        243  |  -.8525952   .4256687    -2.00   0.048     -1.69826   -.0069301
        244  |   -.297423   .3073429    -0.97   0.336    -.9080133    .3131672
        245  |  -.1624693   .3015308    -0.54   0.591    -.7615129    .4365744
        247  |   2.123392   1.256923     1.69   0.095    -.3737049    4.620488
        248  |  -.3645896   .3005503    -1.21   0.228    -.9616852     .232506
        250  |   -.470666   .3682713    -1.28   0.205    -1.202301    .2609693
        251  |  -.3963865   .3740653    -1.06   0.292    -1.139533    .3467595
        252  |  -.2915694   .2944602    -0.99   0.325     -.876566    .2934271
        267  |  -.3910852   .3271475    -1.20   0.235    -1.041021    .2588505
        269  |  -.1624693   .3015308    -0.54   0.591    -.7615129    .4365744
        271  |  -.8990372   .5234743    -1.72   0.089     -1.93901    .1409358
        272  |   -.503325   .3432388    -1.47   0.146    -1.185229    .1785789
        274  |   -.470666   .3682713    -1.28   0.205    -1.202301    .2609693
        275  |  -.5742363   .5120628    -1.12   0.265    -1.591538    .4430658
        276  |   -.297423   .3073429    -0.97   0.336    -.9080133    .3131672
        278  |   -.470666   .3682713    -1.28   0.205    -1.202301    .2609693
        279  |  -.5279694   .4054944    -1.30   0.196    -1.333555    .2776161
        280  |  -.2915694   .2944602    -0.99   0.325     -.876566    .2934271
        283  |  -.1818807     .35486    -0.51   0.610     -.886872    .5231106
        284  |   .3623419   .3376444     1.07   0.286    -.3084476    1.033131
        285  |  -.5085579   .3512719    -1.45   0.151    -1.206421     .189305
        287  |  -.3910852   .3271475    -1.20   0.235    -1.041021    .2588505
             |
      fYes_T |   .3460887    .178231     1.94   0.055    -.0079984    .7001757
        mage |  -.0120104   .0106238    -1.13   0.261    -.0331164    .0090955
    mmarried |    .130239   .2547041     0.51   0.610     -.375775    .6362531
       makan |  -.1910872   .2095356    -0.91   0.364    -.6073663    .2251918
mselfemplo~d |  -.3140977   .1875369    -1.67   0.097    -.6866725    .0584771
       m2q1a |   .0465443   .0458604     1.01   0.313    -.0445653    .1376539
      2.m3q1 |  -.0907505    .233176    -0.39   0.698    -.5539954    .3724944
        trt2 |  -.4390063   .2766246    -1.59   0.116    -.9885694    .1105567
        trt3 |  -.5748369   .2754472    -2.09   0.040    -1.122061    -.027613
        trt4 |  -.5545987   .2799325    -1.98   0.051    -1.110734    .0015362
       _cons |   1.104814   .4470624     2.47   0.015     .2166469    1.992982
------------------------------------------------------------------------------

. boottest trt2, rep($bootstrap_reps) level(95) nogr

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueVendorID, Rademacher weights:
  trt2

                           t(90) =    -1.5870
                        Prob>|t| =     0.0800

95% confidence set for null hypothesis expression: [−.971, .04578]

. boottest trt3, rep($bootstrap_reps) level(95) nogr

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueVendorID, Rademacher weights:
  trt3

                           t(90) =    -2.0869
                        Prob>|t| =     0.0170

95% confidence set for null hypothesis expression: [−1.073, −.104]

. boottest trt4, rep($bootstrap_reps) level(95) nogr

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueVendorID, Rademacher weights:
  trt4

                           t(90) =    -1.9812
                        Prob>|t| =     0.0300

95% confidence set for null hypothesis expression: [−1.09, −.04199]

. reg ihs_fdamt i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q
> 1a i.m3q1 trt2 trt3 trt4, r cluster(uniqueVendorID) level(95)

Linear regression                               Number of obs     =        335
                                                F(76, 90)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6650
                                                Root MSE          =     .51234

                        (Std. err. adjusted for 91 clusters in uniqueVendorID)
------------------------------------------------------------------------------
             |               Robust
   ihs_fdamt | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.2564068   .1952967    -1.31   0.193    -.6443978    .1315842
          5  |   .9801978   .8172561     1.20   0.234    -.6434241     2.60382
          6  |  -.1505601    .266064    -0.57   0.573    -.6791427    .3780226
          7  |  -.2564068   .1952967    -1.31   0.193    -.6443978    .1315842
          8  |   .0018181   .1431441     0.01   0.990    -.2825627     .286199
         11  |   .0175204   .1728071     0.10   0.919     -.325791    .3608318
         14  |   .0175204   .1728071     0.10   0.919     -.325791    .3608318
         18  |  -.1505601    .266064    -0.57   0.573    -.6791427    .3780226
         22  |  -.2564068   .1952967    -1.31   0.193    -.6443978    .1315842
         23  |  -.0144079   .1547576    -0.09   0.926     -.321861    .2930452
         24  |  -.1505601    .266064    -0.57   0.573    -.6791427    .3780226
         26  |  -.1357225    .226643    -0.60   0.551    -.5859884    .3145434
         27  |  -.1505601    .266064    -0.57   0.573    -.6791427    .3780226
         29  |  -.1413738   .2263696    -0.62   0.534    -.5910965     .308349
         30  |  -.1505601    .266064    -0.57   0.573    -.6791427    .3780226
         32  |   .0551518   .2380956     0.23   0.817    -.4178667    .5281704
         33  |  -.1505601    .266064    -0.57   0.573    -.6791427    .3780226
         34  |  -.2564068   .1952967    -1.31   0.193    -.6443978    .1315842
         35  |  -.0627611   .1316199    -0.48   0.635    -.3242471    .1987249
         37  |  -.1559539   .1599472    -0.98   0.332     -.473717    .1618092
         38  |   .0801441   .3498654     0.23   0.819    -.6149245    .7752126
         39  |  -.1563142   .1580139    -0.99   0.325    -.4702363     .157608
         40  |   .4583602   .4653505     0.98   0.327    -.4661398     1.38286
         41  |  -.0566842   .2273376    -0.25   0.804    -.5083301    .3949616
         42  |   .1163507   .2581324     0.45   0.653    -.3964745    .6291758
         51  |    -.17421   .1535896    -1.13   0.260    -.4793425    .1309225
         52  |  -.3578023    .234227    -1.53   0.130    -.8231351    .1075305
         53  |   -.183867    .168133    -1.09   0.277    -.5178926    .1501586
         54  |  -.0692402   .2580723    -0.27   0.789    -.5819459    .4434654
         55  |  -.1402076   .1745188    -0.80   0.424    -.4869196    .2065045
         56  |  -.0115272   .2110952    -0.05   0.957    -.4309047    .4078502
         57  |    1.01254    .481999     2.10   0.038      .054965    1.970115
         58  |   .5954099   .3017358     1.97   0.052     -.004041    1.194861
         61  |  -.1238538   .1946903    -0.64   0.526      -.51064    .2629325
         62  |   -.198671   .1605422    -1.24   0.219     -.517616    .1202741
         63  |  -.2229294    .224971    -0.99   0.324    -.6698736    .2240147
         64  |  -.4257171   .2100702    -2.03   0.046    -.8430582   -.0083759
         65  |   .0181176    .258708     0.07   0.944    -.4958509    .5320861
         66  |   1.850705   .2152256     8.60   0.000     1.423122    2.278288
         67  |   1.046888   .5171462     2.02   0.046     .0194866    2.074289
         68  |  -.4257171   .2100702    -2.03   0.046    -.8430582   -.0083759
         69  |   .1103154   .2092375     0.53   0.599    -.3053714    .5260023
         71  |   .1957817   .2354775     0.83   0.408    -.2720354    .6635988
         72  |  -.4257171   .2100702    -2.03   0.046    -.8430582   -.0083759
         89  |   .0653957   .2038505     0.32   0.749    -.3395889    .4703804
         90  |  -.1908848   .1908745    -1.00   0.320    -.5700903    .1883206
         91  |   -.317212   .2766555    -1.15   0.255    -.8668364    .2324124
         93  |    .099499    .176663     0.56   0.575     -.251473    .4504709
         94  |  -.3305771   .2311826    -1.43   0.156    -.7898617    .1287076
         95  |  -.3120638   .2428714    -1.28   0.202    -.7945703    .1704427
         97  |  -.0509256   .1665945    -0.31   0.761    -.3818948    .2800435
         98  |   -.198671   .1605422    -1.24   0.219     -.517616    .1202741
         99  |  -.2470945   .2637471    -0.94   0.351    -.7710742    .2768853
        102  |   .2754626   .5897643     0.47   0.642    -.8962071    1.447132
        103  |   .9543284   .1609901     5.93   0.000     .6344934    1.274163
        104  |   .2825175   .2454623     1.15   0.253    -.2051361    .7701711
        107  |  -.0107994   .1841386    -0.06   0.953    -.3766227     .355024
        109  |  -.3063068   .2289956    -1.34   0.184    -.7612466     .148633
        110  |  -.1360146    .179676    -0.76   0.451    -.4929723    .2209431
        113  |  -.1525082    .172953    -0.88   0.380    -.4961094     .191093
        114  |  -.3047896   .2930159    -1.04   0.301    -.8869167    .2773375
        117  |  -.1525082    .172953    -0.88   0.380    -.4961094     .191093
        118  |  -.3271514   .1934281    -1.69   0.094    -.7114302    .0571274
        137  |  -.3256472   .1844953    -1.77   0.081    -.6921793    .0408849
        138  |  -.2617407   .1896946    -1.38   0.171     -.638602    .1151207
        141  |  -.4601055   .2014686    -2.28   0.025     -.860358    -.059853
        142  |   -.296401   .1988611    -1.49   0.140    -.6914732    .0986712
        145  |  -.2869665   .1834295    -1.56   0.121    -.6513813    .0774483
        146  |  -.2591942   .1819226    -1.42   0.158    -.6206152    .1022269
        149  |   .5020804   .6650608     0.75   0.452    -.8191789     1.82334
        150  |   .6273444   .2361214     2.66   0.009      .158248    1.096441
        153  |  -.1525082    .172953    -0.88   0.380    -.4961094     .191093
        154  |  -.0795188   .2240036    -0.35   0.723     -.524541    .3655034
        157  |  -.1107266   .2183664    -0.51   0.613    -.5445496    .3230965
        158  |  -.0110625   .2649507    -0.04   0.967    -.5374332    .5153082
        159  |   .0089598   .1633512     0.05   0.956    -.3155659    .3334854
        160  |   1.921319   .2303782     8.34   0.000     1.463632    2.379005
        162  |   .7079798   .2303782     3.07   0.003     .2502933    1.165666
        171  |  -.2304129   .2770694    -0.83   0.408    -.7808596    .3200339
        172  |   .1512688   .2188729     0.69   0.491    -.2835604     .586098
        173  |  -.2304129   .2770694    -0.83   0.408    -.7808596    .3200339
        174  |  -.1733938   .2303782    -0.75   0.454    -.6310802    .2842927
        175  |  -.1107266   .2183664    -0.51   0.613    -.5445496    .3230965
        176  |  -.1733938   .2303782    -0.75   0.454    -.6310802    .2842927
        177  |   .7171943   .1792433     4.00   0.000     .3610962    1.073292
        178  |   .4296243   .3457368     1.24   0.217    -.2572423    1.116491
        180  |   .8981608   .7635664     1.18   0.243    -.6187972    2.415119
        181  |  -.3917694   .2354279    -1.66   0.100     -.859488    .0759492
        182  |  -.0938236   .1910983    -0.49   0.625    -.4734737    .2858265
        183  |   1.857141   .1880585     9.88   0.000      1.48353    2.230752
        184  |   .4888672   .8398118     0.58   0.562    -1.179566      2.1573
        185  |  -.4088319   .2074166    -1.97   0.052    -.8209013    .0032374
        186  |  -.0703179   .2217074    -0.32   0.752    -.5107784    .3701426
        187  |  -.2375712   .1880585    -1.26   0.210    -.6111823    .1360399
        189  |  -.2375712   .1880585    -1.26   0.210    -.6111823    .1360399
        191  |  -.2375712   .1880585    -1.26   0.210    -.6111823    .1360399
        193  |  -.2375712   .1880585    -1.26   0.210    -.6111823    .1360399
        195  |   -.306139   .1965994    -1.56   0.123    -.6967181      .08444
        196  |   1.053771    1.02213     1.03   0.305    -.9768683     3.08441
        197  |   .0111342   .4034996     0.03   0.978    -.7904881    .8127565
        198  |  -.2307893   .1843135    -1.25   0.214    -.5969602    .1353815
        199  |   -.282657    .173935    -1.63   0.108    -.6282093    .0628953
        200  |   -.128065    .182812    -0.70   0.485     -.491253     .235123
        201  |   1.495245   .1914286     7.81   0.000     1.114939    1.875551
        202  |   2.254788   .1533336    14.71   0.000     1.950164    2.559412
        203  |   2.074867   .1880585    11.03   0.000     1.701256    2.448478
        204  |   -.140835   .1912261    -0.74   0.463     -.520739    .2390689
        205  |    -.11519   .1884515    -0.61   0.543    -.4895817    .2592017
        206  |    .967335   .9026167     1.07   0.287    -.8258707    2.760541
        207  |   .4211473   .6398952     0.66   0.512    -.8501162    1.692411
        210  |  -.1410476   .1951986    -0.72   0.472    -.5288438    .2467485
        211  |  -.2564736   .1963701    -1.31   0.195    -.6465971    .1336498
        212  |  -.2023455   .1804769    -1.12   0.265    -.5608944    .1562033
        213  |  -.1749241   .2532131    -0.69   0.491    -.6779762     .328128
        214  |  -.2854665   .2687776    -1.06   0.291    -.8194401    .2485071
        215  |    .679028   .1804769     3.76   0.000     .3204792    1.037577
        216  |   .7968957   .7499532     1.06   0.291    -.6930172    2.286809
        217  |   .6982033   .8427043     0.83   0.410    -.9759758    2.372382
        219  |   .7681745   .8007556     0.96   0.340    -.8226663    2.359015
        221  |   .4358363   .3231465     1.35   0.181    -.2061505    1.077823
        227  |   -.179868   .2357729    -0.76   0.448    -.6482719     .288536
        231  |  -.1303224    .212161    -0.61   0.541    -.5518173    .2911725
        233  |  -.0768013   .1952908    -0.39   0.695    -.4647806    .3111779
        235  |  -.1738101   .1958137    -0.89   0.377    -.5628281    .2152079
        237  |   1.253647   .4189516     2.99   0.004     .4213267    2.085968
        239  |   2.378693   .3163546     7.52   0.000       1.7502    3.007187
        241  |  -.0858361   .1615729    -0.53   0.597    -.4068289    .2351567
        243  |  -.4778774   .2237288    -2.14   0.035    -.9223537   -.0334011
        244  |  -.1607287   .1700176    -0.95   0.347    -.4984984     .177041
        245  |  -.0858361   .1615729    -0.53   0.597    -.4068289    .2351567
        247  |   1.095891   .6415402     1.71   0.091    -.1786405    2.370423
        248  |  -.2065414   .1645458    -1.26   0.213    -.5334403    .1203575
        250  |  -.2466862   .1995882    -1.24   0.220     -.643203    .1498306
        251  |  -.1028593   .2617386    -0.39   0.695    -.6228487    .4171301
        252  |   -.170457   .1600178    -1.07   0.290    -.4883603    .1474463
        267  |  -.2035184   .1753544    -1.16   0.249    -.5518905    .1448537
        269  |  -.0858361   .1615729    -0.53   0.597    -.4068289    .2351567
        271  |  -.4932165   .2820629    -1.75   0.084    -1.053584    .0671507
        272  |  -.2519706    .183227    -1.38   0.172    -.6159829    .1120418
        274  |  -.2466862   .1995882    -1.24   0.220     -.643203    .1498306
        275  |    -.31081   .2800493    -1.11   0.270    -.8671768    .2455568
        276  |  -.1607287   .1700176    -0.95   0.347    -.4984984     .177041
        278  |  -.2466862   .1995882    -1.24   0.220     -.643203    .1498306
        279  |  -.2612356   .2173689    -1.20   0.233    -.6930768    .1706055
        280  |   -.170457   .1600178    -1.07   0.290    -.4883603    .1474463
        283  |  -.0880967   .1943064    -0.45   0.651    -.4741203    .2979269
        284  |   .5377776   .1836657     2.93   0.004     .1728935    .9026616
        285  |  -.2589751   .1888838    -1.37   0.174    -.6342258    .1162756
        287  |  -.2035184   .1753544    -1.16   0.249    -.5518905    .1448537
             |
      fYes_T |    .173139   .0897987     1.93   0.057    -.0052617    .3515397
        mage |  -.0066695   .0056833    -1.17   0.244    -.0179604    .0046214
    mmarried |   .0552353   .1320831     0.42   0.677    -.2071708    .3176414
       makan |   -.096501   .1106954    -0.87   0.386    -.3164168    .1234148
mselfemplo~d |  -.1681374   .0952985    -1.76   0.081    -.3574646    .0211897
       m2q1a |   .0255051   .0254976     1.00   0.320    -.0251504    .0761605
      2.m3q1 |  -.0575079   .1251025    -0.46   0.647    -.3060458    .1910299
        trt2 |  -.2485905   .1489922    -1.67   0.099    -.5445896    .0474085
        trt3 |  -.3413888   .1489771    -2.29   0.024    -.6373579   -.0454198
        trt4 |  -.3253692   .1486821    -2.19   0.031    -.6207522   -.0299861
       _cons |   .6179364   .2349796     2.63   0.010     .1511084    1.084764
------------------------------------------------------------------------------

. boottest trt2, rep($bootstrap_reps) level(95) nogr

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueVendorID, Rademacher weights:
  trt2

                           t(90) =    -1.6685
                        Prob>|t| =     0.0460

95% confidence set for null hypothesis expression: [−.529, −.001958]

. boottest trt3, rep($bootstrap_reps) level(95) nogr

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueVendorID, Rademacher weights:
  trt3

                           t(90) =    -2.2916
                        Prob>|t| =     0.0140

95% confidence set for null hypothesis expression: [−.6257, −.06745]

. boottest trt4, rep($bootstrap_reps) level(95) nogr

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueVendorID, Rademacher weights:
  trt4

                           t(90) =    -2.1884
                        Prob>|t| =     0.0190

95% confidence set for null hypothesis expression: [−.6021, −.06373]

. *randomization inf: permutation test, pval
. ritest trt2 trt3 trt4 _b[trt2] _b[trt3] _b[trt4], reps($bootstrap_reps) clus
> ter(uniqueVendorID) strata(ge01) seed(546): reg fd i.distXtrXdateFes fYes_T 
> mage mmarried makan mselfemployed m2q1a i.m3q1 trt2 trt3 trt4
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       335
-------------+----------------------------------   F(159, 175)     =      2.42
       Model |  32.9773638       159  .207404804   Prob > F        =    0.0000
    Residual |  14.9808451       175  .085604829   R-squared       =    0.6876
-------------+----------------------------------   Adj R-squared   =    0.4038
       Total |   47.958209       334  .143587452   Root MSE        =    .29258

------------------------------------------------------------------------------
          fd | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.1720881   .3450629    -0.50   0.619    -.8531086    .5089324
          5  |   .4065839   .2517611     1.61   0.108     -.090295    .9034629
          6  |  -.0451877   .3577167    -0.13   0.900    -.7511819    .6608065
          7  |  -.1720881   .3450629    -0.50   0.619    -.8531086    .5089324
          8  |   .0127877   .2295274     0.06   0.956    -.4402104    .4657857
         11  |   .0228261   .3481228     0.07   0.948    -.6642334    .7098856
         14  |   .0228261   .3481228     0.07   0.948    -.6642334    .7098856
         18  |  -.0451877   .3577167    -0.13   0.900    -.7511819    .6608065
         22  |  -.1720881   .3450629    -0.50   0.619    -.8531086    .5089324
         23  |   .0008988   .2444033     0.00   0.997    -.4814586    .4832563
         24  |  -.0451877   .3577167    -0.13   0.900    -.7511819    .6608065
         26  |  -.0747341   .2257859    -0.33   0.741    -.5203479    .3708797
         27  |  -.0451877   .3577167    -0.13   0.900    -.7511819    .6608065
         29  |  -.0861459   .2305212    -0.37   0.709    -.5411053    .3688136
         30  |  -.0451877   .3577167    -0.13   0.900    -.7511819    .6608065
         32  |   .1149532   .2156643     0.53   0.595    -.3106844    .5405909
         33  |  -.0451877   .3577167    -0.13   0.900    -.7511819    .6608065
         34  |  -.1720881   .3450629    -0.50   0.619    -.8531086    .5089324
         35  |     -.0185   .2473422    -0.07   0.940    -.5066576    .4696577
         37  |  -.0795245   .2013966    -0.39   0.693    -.4770032    .3179543
         38  |  -.0011219   .2107929    -0.01   0.996    -.4171453    .4149015
         39  |  -.0795102   .2421593    -0.33   0.743    -.5574387    .3984184
         40  |   .2021132   .2073059     0.97   0.331    -.2070283    .6112548
         41  |   .0598904   .2127241     0.28   0.779    -.3599444    .4797253
         42  |   .3120794   .2344377     1.33   0.185    -.1506098    .7747687
         51  |  -.0926029   .2416912    -0.38   0.702    -.5696077    .3844019
         52  |  -.2089177    .247983    -0.84   0.401    -.6983401    .2805047
         53  |  -.0869915   .2459145    -0.35   0.724    -.5723315    .3983485
         54  |  -.0301392   .2494458    -0.12   0.904    -.5224485    .4621701
         55  |  -.0745278   .2271439    -0.33   0.743    -.5228219    .3737662
         56  |  -.0008763   .2478894    -0.00   0.997    -.4901139    .4883614
         57  |   .8308642   .2760512     3.01   0.003     .2860462    1.375682
         58  |   .8300699   .2734143     3.04   0.003      .290456    1.369684
         61  |  -.0652686   .2477648    -0.26   0.793    -.5542603    .4237231
         62  |  -.1204084   .2728028    -0.44   0.659    -.6588154    .4179986
         63  |  -.1133814   .2485971    -0.46   0.649    -.6040158     .377253
         64  |  -.2580322   .3462663    -0.75   0.457    -.9414278    .4253633
         65  |   .0158098   .2459222     0.06   0.949    -.4695454    .5011649
         66  |   .8411558   .2768447     3.04   0.003     .2947716     1.38754
         67  |   .4761427   .2226893     2.14   0.034     .0366404     .915645
         68  |  -.2580322   .3462663    -0.75   0.457    -.9414278    .4253633
         69  |   .0717065   .3473752     0.21   0.837    -.6138774    .7572905
         71  |   .3186749   .2193958     1.45   0.148    -.1143273    .7516772
         72  |  -.2580322   .3462663    -0.75   0.457    -.9414278    .4253633
         89  |   .0372121   .2432373     0.15   0.879    -.4428441    .5172682
         90  |  -.1370349   .3447949    -0.40   0.692    -.8175264    .5434565
         91  |  -.1735912   .2533562    -0.69   0.494    -.6736181    .3264357
         93  |   .0619154   .2309978     0.27   0.789    -.3939846    .5178155
         94  |  -.2027385   .2796393    -0.73   0.469    -.7546381     .349161
         95  |  -.1738935   .2370367    -0.73   0.464     -.641712    .2939251
         97  |  -.0204617   .2306636    -0.09   0.929    -.4757023    .4347789
         98  |  -.1204084   .2728028    -0.44   0.659    -.6588154    .4179986
         99  |  -.1407087   .2368041    -0.59   0.553    -.6080682    .3266509
        102  |   .4016768     .27287     1.47   0.143    -.1368628    .9402163
        103  |   1.057077   .2735257     3.86   0.000     .5172436    1.596911
        104  |    .654179   .3529747     1.85   0.066    -.0424563    1.350814
        107  |   .0132359   .2485148     0.05   0.958    -.4772359    .5037078
        109  |  -.1638366   .2723581    -0.60   0.548     -.701366    .3736928
        110  |  -.0760628   .2300746    -0.33   0.741     -.530141    .3780154
        113  |  -.0923236    .341534    -0.27   0.787    -.7663794    .5817322
        114  |  -.1795011   .2732962    -0.66   0.512    -.7188818    .3598797
        117  |  -.0923236    .341534    -0.27   0.787    -.7663794    .5817322
        118  |   -.205686   .2292251    -0.90   0.371    -.6580874    .2467155
        137  |  -.1801123   .3463787    -0.52   0.604    -.8637298    .5035051
        138  |  -.1530189   .2462965    -0.62   0.535    -.6391126    .3330749
        141  |  -.2353496   .3484816    -0.68   0.500    -.9231172     .452418
        142  |  -.2132201   .3448747    -0.62   0.537    -.8938692     .467429
        145  |  -.1475608   .3437717    -0.43   0.668     -.826033    .5309113
        146  |  -.1599085   .2475511    -0.65   0.519    -.6484784    .3286614
        149  |   .3800578   .2708772     1.40   0.162    -.1545488    .9146644
        150  |   .8495581   .2433948     3.49   0.001     .3691911    1.329925
        153  |  -.0923236    .341534    -0.27   0.787    -.7663794    .5817322
        154  |  -.0275626   .3476419    -0.08   0.937    -.7136729    .6585477
        157  |  -.0870981   .2726868    -0.32   0.750    -.6252762    .4510801
        158  |  -.0042501   .2817529    -0.02   0.988    -.5603211     .551821
        159  |  -.0129404   .3418615    -0.04   0.970    -.6876424    .6617616
        160  |   .8828688   .3523621     2.51   0.013     .1874426    1.578295
        162  |   .8828688   .3523621     2.51   0.013     .1874426    1.578295
        171  |  -.1612557   .3495492    -0.46   0.645    -.8511304     .528619
        172  |   .1086311   .3517282     0.31   0.758     -.585544    .8028063
        173  |  -.1612557   .3495492    -0.46   0.645    -.8511304     .528619
        174  |  -.1171312   .3523621    -0.33   0.740    -.8125574    .5782949
        175  |  -.0870981   .2726868    -0.32   0.750    -.6252762    .4510801
        176  |  -.1171312   .3523621    -0.33   0.740    -.8125574    .5782949
        177  |   .8992708   .3463676     2.60   0.010     .2156754    1.582866
        178  |   .4957499   .2817529     1.76   0.080    -.0603211    1.051821
        180  |   .4957499   .2817529     1.76   0.080    -.0603211    1.051821
        181  |  -.2271411   .2739627    -0.83   0.408    -.7678373    .3135551
        182  |  -.0425617   .2466179    -0.17   0.863    -.5292898    .4441663
        183  |   .8364833    .344068     2.43   0.016     .1574265     1.51554
        184  |   .2343429   .2512878     0.93   0.352    -.2616018    .7302876
        185  |  -.2209592    .349327    -0.63   0.528    -.9103953     .468477
        186  |  -.0306343   .2754935    -0.11   0.912    -.5743516    .5130831
        187  |  -.1635167    .344068    -0.48   0.635    -.8425735    .5155401
        189  |  -.1635167    .344068    -0.48   0.635    -.8425735    .5155401
        191  |  -.1635167    .344068    -0.48   0.635    -.8425735    .5155401
        193  |  -.1635167    .344068    -0.48   0.635    -.8425735    .5155401
        195  |  -.1984199   .2745389    -0.72   0.471    -.7402532    .3434134
        196  |   .4560307   .2735107     1.67   0.097    -.0837735    .9958348
        197  |   .1566633   .2439515     0.64   0.522    -.3248024     .638129
        198  |  -.1133586   .3481648    -0.33   0.745     -.800501    .5737838
        199  |    -.17667   .2439515    -0.72   0.470    -.6581358    .3047957
        200  |  -.0602609   .2319257    -0.26   0.795    -.5179924    .3974705
        201  |   .8077621   .2735523     2.95   0.004     .2678759    1.347648
        202  |   .9744302   .3424817     2.85   0.005     .2985041    1.650356
        203  |   .8364833    .344068     2.43   0.016     .1574265     1.51554
        204  |  -.0664166   .3467945    -0.19   0.848    -.7508546    .6180214
        205  |  -.0698484   .2178425    -0.32   0.749    -.4997852    .3600883
        206  |   .4556583   .2759734     1.65   0.101    -.0890062    1.000323
        207  |   .1733344   .2330877     0.74   0.458    -.2866904    .6333591
        210  |  -.0811929   .2425829    -0.33   0.738    -.5599575    .3975718
        211  |  -.1397187   .2456997    -0.57   0.570    -.6246347    .3451973
        212  |  -.1142549   .3438553    -0.33   0.740     -.792892    .5643823
        213  |  -.0931099   .2480529    -0.38   0.708    -.5826701    .3964503
        214  |  -.1431088   .2817785    -0.51   0.612    -.6992303    .4130128
        215  |   .8857451   .3438553     2.58   0.011      .207108    1.564382
        216  |     .44468   .2751214     1.62   0.108    -.0983029     .987663
        217  |   .3062412   .2490884     1.23   0.221    -.1853628    .7978451
        219  |   .3561232   .2406662     1.48   0.141    -.1188586    .8311051
        221  |   .5878637   .2456897     2.39   0.018     .1029675     1.07276
        227  |  -.0942547   .3513664    -0.27   0.789    -.7877158    .5992065
        231  |  -.0563551   .2518784    -0.22   0.823    -.5534654    .4407553
        233  |   -.020136    .248184    -0.08   0.935     -.509955     .469683
        235  |  -.0765606    .234851    -0.33   0.745    -.5400655    .3869443
        237  |   .9711822   .2787079     3.48   0.001     .4211209    1.521243
        239  |    .960074   .2745218     3.50   0.001     .4182743    1.501874
        241  |  -.0360592   .3436645    -0.10   0.917    -.7143197    .6422013
        243  |  -.3039526   .3453311    -0.88   0.380    -.9855022    .3775971
        244  |  -.0831659   .2695952    -0.31   0.758    -.6152423    .4489105
        245  |  -.0360592   .3436645    -0.10   0.917    -.7143197    .6422013
        247  |   .4685145   .2453354     1.91   0.058    -.0156825    .9527115
        248  |      -.098   .2716385    -0.36   0.719    -.6341091    .4381092
        250  |  -.1351809   .3475648    -0.39   0.698    -.8211391    .5507773
        251  |   .0907317   .2495399     0.36   0.717    -.4017633    .5832268
        252  |  -.0693651   .3423157    -0.20   0.840    -.7449636    .6062333
        267  |  -.1170327   .3425389    -0.34   0.733    -.7930718    .5590064
        269  |  -.0360592   .3436645    -0.10   0.917    -.7143197    .6422013
        271  |  -.3053861   .2826266    -1.08   0.281    -.8631814    .2524093
        272  |  -.1274858   .3477488    -0.37   0.714    -.8138071    .5588356
        274  |  -.1351809   .3475648    -0.39   0.698    -.8211391    .5507773
        275  |  -.1991078   .2490139    -0.80   0.425    -.6905647     .292349
        276  |  -.0831659   .2695952    -0.31   0.758    -.6152423    .4489105
        278  |  -.1351809   .3475648    -0.39   0.698    -.8211391    .5507773
        279  |   -.147147   .2770106    -0.53   0.596    -.6938585    .3995646
        280  |  -.0693651   .3423157    -0.20   0.840    -.7449636    .6062333
        283  |  -.0593582   .2706565    -0.22   0.827    -.5935292    .4748127
        284  |   .8428461    .347582     2.42   0.016     .1568539    1.528838
        285  |   -.123848    .349668    -0.35   0.724    -.8139571    .5662612
        287  |  -.1170327   .3425389    -0.34   0.733    -.7930718    .5590064
             |
      fYes_T |   .0877888   .0580959     1.51   0.133      -.02687    .2024476
        mage |  -.0037831   .0032936    -1.15   0.252    -.0102834    .0027172
    mmarried |   .0189093    .058955     0.32   0.749     -.097445    .1352637
       makan |  -.0668464   .0531665    -1.26   0.210    -.1717764    .0380837
mselfemplo~d |  -.0831663   .0457394    -1.82   0.071     -.173438    .0071055
       m2q1a |   .0123546   .0131811     0.94   0.350    -.0136597    .0383689
      2.m3q1 |  -.0426023   .0668885    -0.64   0.525    -.1746143    .0894097
        trt2 |   -.184884   .0775842    -2.38   0.018    -.3380051   -.0317628
        trt3 |    -.21733   .0642642    -3.38   0.001    -.3441626   -.0904974
        trt4 |   -.211629   .0625114    -3.39   0.001    -.3350022   -.0882558
       _cons |   .3833977   .1941714     1.97   0.050     .0001787    .7666168
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000
Warning: 100% of the resampled realizations for _pm_1 are exactly identical to
>  original value
Warning: 100% of the resampled realizations for _pm_2 are exactly identical to
>  original value

      Command: regress fd i.distXtrXdateFes fYes_T mage mmarried makan
                   mselfemployed m2q1a i.m3q1 trt2 trt3 trt4
        _pm_1: trt3
        _pm_2: trt4
        _pm_3: _b[trt2]
        _pm_4: _b[trt3]
        _pm_5: _b[trt4]
  res. var(s):  trt2
   Resampling:  Permuting trt2
Clust. var(s):  uniqueVendorID
     Clusters:  207
Strata var(s):  ge01
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_2 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_3 |   -.184884       4    1000  0.0040  0.0020  .0010909   .0102097
       _pm_4 |    -.21733       0    1000  0.0000  0.0000         0   .0036821
       _pm_5 |   -.211629       0    1000  0.0000  0.0000         0   .0036821
------------------------------------------------------------------------------
Note: Confidence intervals are with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. ritest trt2 trt3 trt4 _b[trt2] _b[trt3] _b[trt4], reps($bootstrap_reps) clus
> ter(uniqueVendorID) strata(ge01) seed(546): reg fdamt i.distXtrXdateFes fYes
> _T mage mmarried makan mselfemployed m2q1a i.m3q1 trt2 trt3 trt4
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       335
-------------+----------------------------------   F(159, 175)     =      2.17
       Model |  336.592996       159  2.11693708   Prob > F        =    0.0000
    Residual |  170.762228       175  .975784162   R-squared       =    0.6634
-------------+----------------------------------   Adj R-squared   =    0.3576
       Total |  507.355224       334  1.51902762   Root MSE        =    .98782

------------------------------------------------------------------------------
       fdamt | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.4269911      1.165    -0.37   0.714     -2.72625    1.872268
          5  |   1.928633   .8499952     2.27   0.024     .2510721    3.606194
          6  |  -.2788827   1.207722    -0.23   0.818    -2.662458    2.104693
          7  |  -.4269911      1.165    -0.37   0.714     -2.72625    1.872268
          8  |  -.0189659   .7749296    -0.02   0.981    -1.548377    1.510445
         11  |   .0040794   1.175331     0.00   0.997    -2.315569    2.323728
         14  |   .0040794   1.175331     0.00   0.997    -2.315569    2.323728
         18  |  -.2788827   1.207722    -0.23   0.818    -2.662458    2.104693
         22  |  -.4269911      1.165    -0.37   0.714     -2.72625    1.872268
         23  |  -.0335199   .8251537    -0.04   0.968    -1.662054    1.595014
         24  |  -.2788827   1.207722    -0.23   0.818    -2.662458    2.104693
         26  |  -.2597124   .7622975    -0.34   0.734    -1.764192    1.244768
         27  |  -.2788827   1.207722    -0.23   0.818    -2.662458    2.104693
         29  |  -.2608157   .7782849    -0.34   0.738    -1.796848    1.275217
         30  |  -.2788827   1.207722    -0.23   0.818    -2.662458    2.104693
         32  |  -.0118559    .728125    -0.02   0.987    -1.448892    1.425181
         33  |  -.2788827   1.207722    -0.23   0.818    -2.662458    2.104693
         34  |  -.4269911      1.165    -0.37   0.714     -2.72625    1.872268
         35  |   -.142026   .8350759    -0.17   0.865    -1.790142     1.50609
         37  |  -.2998468   .6799544    -0.44   0.660    -1.641813     1.04212
         38  |   .2408533   .7116782     0.34   0.735    -1.163724     1.64543
         39  |  -.2844996   .8175773    -0.35   0.728     -1.89808    1.329081
         40  |   .8709117   .6999055     1.24   0.215    -.5104305    2.252254
         41  |  -.2288246   .7181983    -0.32   0.750     -1.64627     1.18862
         42  |  -.1177637   .7915079    -0.15   0.882    -1.679894    1.444366
         51  |  -.3276455   .8159971    -0.40   0.689    -1.938108    1.282817
         52  |   -.685703   .8372394    -0.82   0.414    -2.338089    .9666832
         53  |  -.3605983   .8302559    -0.43   0.665    -1.999202    1.278005
         54  |  -.1727535    .842178    -0.21   0.838    -1.834887     1.48938
         55  |   -.258522   .7668825    -0.34   0.736    -1.772051    1.255007
         56  |  -.0573906   .8369235    -0.07   0.945    -1.709153    1.594372
         57  |   1.353872   .9320031     1.45   0.148    -.4855412    3.193285
         58  |    .465996   .9231004     0.50   0.614    -1.355846    2.287838
         61  |  -.2705503   .8365027    -0.32   0.747    -1.921482    1.380382
         62  |  -.3645668   .9210359    -0.40   0.693    -2.182335    1.453201
         63  |  -.4159627   .8393128    -0.50   0.621    -2.072441    1.240516
         64  |  -.7425862   1.169063    -0.64   0.526    -3.049864    1.564692
         65  |   .0023549   .8302818     0.00   0.998      -1.6363    1.641009
         66  |   3.579952   .9346823     3.83   0.000     1.735251    5.424653
         67  |    2.00912   .7518428     2.67   0.008     .5252742    3.492967
         68  |  -.7425862   1.169063    -0.64   0.526    -3.049864    1.564692
         69  |   .1800416   1.172807     0.15   0.878    -2.134625    2.494708
         71  |   .1152452   .7407234     0.16   0.877    -1.346656    1.577146
         72  |  -.7425862   1.169063    -0.64   0.526    -3.049864    1.564692
         89  |   .1020436   .8212169     0.12   0.901     -1.51872    1.722808
         90  |  -.3083842   1.164095    -0.26   0.791    -2.605857    1.989089
         91  |  -.5881867   .8553803    -0.69   0.493    -2.276376    1.100003
         93  |   .1550407   .7798939     0.20   0.843    -1.384168    1.694249
         94  |  -.5930923   .9441172    -0.63   0.531    -2.456414    1.270229
         95  |  -.5938275   .8002824    -0.74   0.459    -2.173275    .9856199
         97  |   -.129269   .7787658    -0.17   0.868    -1.666251    1.407713
         98  |  -.3645668   .9210359    -0.40   0.693    -2.182335    1.453201
         99  |  -.4719364   .7994973    -0.59   0.556    -2.049834    1.105961
        102  |   .1968137   .9212627     0.21   0.831    -1.621402    2.015029
        103  |   1.135299   .9234764     1.23   0.221    -.6872851    2.957884
        104  |  -.0886749   1.191712    -0.07   0.941    -2.440653    2.263303
        107  |   -.034553   .8390348    -0.04   0.967    -1.690483    1.621377
        109  |  -.5813866   .9195346    -0.63   0.528    -2.396192    1.233418
        110  |  -.2711185   .7767773    -0.35   0.727    -1.804176    1.261939
        113  |  -.2979966   1.153086    -0.26   0.796    -2.573742    1.977748
        114  |  -.5561836   .9227017    -0.60   0.547    -2.377239    1.264872
        117  |  -.2979966   1.153086    -0.26   0.796    -2.573742    1.977748
        118  |  -.6048832    .773909    -0.78   0.436     -2.13228    .9225132
        137  |  -.6440853   1.169443    -0.55   0.582    -2.952112    1.663942
        138  |   -.495564   .8315453    -0.60   0.552    -2.136712    1.145584
        141  |  -.8647766   1.176542    -0.74   0.463    -3.186815    1.457262
        142  |  -.5144767   1.164365    -0.44   0.659    -2.812482    1.783529
        145  |   -.518688   1.160641    -0.45   0.656    -2.809343    1.771967
        146  |   -.488688   .8357811    -0.58   0.559    -2.138196     1.16082
        149  |   .5916577   .9145346     0.65   0.519    -1.213279    2.396595
        150  |   .5298788   .8217487     0.64   0.520    -1.091935    2.151692
        153  |  -.2979966   1.153086    -0.26   0.796    -2.573742    1.977748
        154  |  -.1651858   1.173707    -0.14   0.888    -2.481629    2.151258
        157  |  -.2114699   .9206444    -0.23   0.819    -2.028465    1.605525
        158  |   -.028726   .9512533    -0.03   0.976    -1.906131    1.848679
        159  |   .0040907   1.154192     0.00   0.997    -2.273836    2.282017
        160  |   3.710304   1.189644     3.12   0.002     1.362408    6.058199
        162  |   .7103037   1.189644     0.60   0.551    -1.637592    3.058199
        171  |  -.4270305   1.180147    -0.36   0.718    -2.756183    1.902122
        172  |   .2322442   1.187504     0.20   0.845    -2.111428    2.575916
        173  |  -.4270305   1.180147    -0.36   0.718    -2.756183    1.902122
        174  |  -.2896963   1.189644    -0.24   0.808    -2.637592    2.058199
        175  |  -.2114699   .9206444    -0.23   0.819    -2.028465    1.605525
        176  |  -.2896963   1.189644    -0.24   0.808    -2.637592    2.058199
        177  |    .658002   1.169405     0.56   0.574    -1.649951    2.965955
        178  |    .471274   .9512533     0.50   0.621    -1.406131    2.348679
        180  |   1.471274   .9512533     1.55   0.124    -.4061314    3.348679
        181  |  -.7226131    .924952    -0.78   0.436     -2.54811    1.102884
        182  |  -.2129711   .8326304    -0.26   0.798    -1.856261    1.430319
        183  |   3.607543   1.161641     3.11   0.002     1.314913    5.900172
        184  |   .9070283    .848397     1.07   0.286    -.7673786    2.581435
        185  |  -.7957286   1.179397    -0.67   0.501    -3.123401    1.531944
        186  |  -.1837812   .9301201    -0.20   0.844    -2.019478    1.651915
        187  |  -.3924572   1.161641    -0.34   0.736    -2.685087    1.900172
        189  |  -.3924572   1.161641    -0.34   0.736    -2.685087    1.900172
        191  |  -.3924572   1.161641    -0.34   0.736    -2.685087    1.900172
        193  |  -.3924572   1.161641    -0.34   0.736    -2.685087    1.900172
        195  |  -.5209774   .9268972    -0.56   0.575    -2.350313    1.308359
        196  |   2.277048    .923426     2.47   0.015     .4545627    4.099533
        197  |  -.1638649   .8236283    -0.20   0.843    -1.789388    1.461658
        198  |  -.4657963   1.175473    -0.40   0.692    -2.785724    1.854132
        199  |  -.4971982   .8236283    -0.60   0.547    -2.122721    1.128325
        200  |  -.2761774   .7830269    -0.35   0.725    -1.821569    1.269214
        201  |   2.405907   .9235663     2.61   0.010     .5831452    4.228669
        202  |   4.880292   1.156286     4.22   0.000     2.598233    7.162352
        203  |   4.607543   1.161641     3.97   0.000     2.314913    6.900172
        204  |  -.2713509   1.170847    -0.23   0.817    -2.582148    2.039446
        205  |   -.226848   .7354793    -0.31   0.758    -1.678399    1.224703
        206  |   1.816047   .9317404     1.95   0.053    -.0228478    3.654941
        207  |   .9123095   .7869498     1.16   0.248    -.6408245    2.465443
        210  |  -.2701289   .8190076    -0.33   0.742    -1.886533    1.346275
        211  |  -.5008547   .8295305    -0.60   0.547    -2.138026    1.136317
        212  |  -.3895878   1.160923    -0.34   0.738      -2.6808    1.901625
        213  |   -.363765   .8374753    -0.43   0.665    -2.016617    1.289087
        214  |  -.5765764   .9513396    -0.61   0.545    -2.454152    1.300999
        215  |   .6104122   1.160923     0.53   0.600      -1.6808    2.901625
        216  |   1.269512   .9288639     1.37   0.173     -.563705     3.10273
        217  |   1.481846   .8409714     1.76   0.080    -.1779056    3.141598
        219  |   1.440542   .8125365     1.77   0.078    -.1630905    3.044174
        221  |   .3816091   .8294967     0.46   0.646    -1.255496    2.018714
        227  |  -.4114752   1.186282    -0.35   0.729    -2.752737    1.929786
        231  |  -.3001836   .8503911    -0.35   0.725    -1.978526    1.378159
        233  |  -.1588694    .837918    -0.19   0.850    -1.812595    1.494856
        235  |  -.3618312   .7929033    -0.46   0.649    -1.926715    1.203053
        237  |   1.814213   .9409726     1.93   0.055    -.0429025    3.671328
        239  |   5.800496   .9268397     6.26   0.000     3.971274    7.629718
        241  |  -.1624693   1.160279    -0.14   0.889     -2.45241    2.127472
        243  |  -.8525952   1.165906    -0.73   0.466    -3.153641    1.448451
        244  |   -.297423   .9102063    -0.33   0.744    -2.093818    1.498971
        245  |  -.1624693   1.160279    -0.14   0.889     -2.45241    2.127472
        247  |   2.123392   .8283006     2.56   0.011     .4886473    3.758136
        248  |  -.3645896   .9171051    -0.40   0.691      -2.1746     1.44542
        250  |   -.470666   1.173447    -0.40   0.689    -2.786596    1.845264
        251  |  -.3963865   .8424958    -0.47   0.639    -2.059147    1.266374
        252  |  -.2915694   1.155725    -0.25   0.801    -2.572523    1.989384
        267  |  -.3910852   1.156479    -0.34   0.736    -2.673526    1.891356
        269  |  -.1624693   1.160279    -0.14   0.889     -2.45241    2.127472
        271  |  -.8990372   .9542029    -0.94   0.347    -2.782264    .9841897
        272  |   -.503325   1.174068    -0.43   0.669    -2.820481    1.813831
        274  |   -.470666   1.173447    -0.40   0.689    -2.786596    1.845264
        275  |  -.5742363   .8407198    -0.68   0.495    -2.233491    1.085019
        276  |   -.297423   .9102063    -0.33   0.744    -2.093818    1.498971
        278  |   -.470666   1.173447    -0.40   0.689    -2.786596    1.845264
        279  |  -.5279694   .9352422    -0.56   0.573    -2.373775    1.317836
        280  |  -.2915694   1.155725    -0.25   0.801    -2.572523    1.989384
        283  |  -.1818807   .9137895    -0.20   0.842    -1.985347    1.621586
        284  |   .3623419   1.173505     0.31   0.758    -1.953703    2.678387
        285  |  -.5085579   1.180548    -0.43   0.667    -2.838502    1.821386
        287  |  -.3910852   1.156479    -0.34   0.736    -2.673526    1.891356
             |
      fYes_T |   .3460887   .1961432     1.76   0.079     -.041022    .7331993
        mage |  -.0120104   .0111199    -1.08   0.282    -.0339568    .0099359
    mmarried |    .130239   .1990437     0.65   0.514    -.2625961    .5230742
       makan |  -.1910872   .1795005    -1.06   0.289    -.5453516    .1631772
mselfemplo~d |  -.3140977   .1544251    -2.03   0.043    -.6188729   -.0093225
       m2q1a |   .0465443   .0445018     1.05   0.297    -.0412851    .1343737
      2.m3q1 |  -.0907505   .2258288    -0.40   0.688     -.536449     .354948
        trt2 |  -.4390063   .2619395    -1.68   0.096    -.9559734    .0779608
        trt3 |  -.5748369   .2169686    -2.65   0.009    -1.003049    -.146625
        trt4 |  -.5545987   .2110507    -2.63   0.009    -.9711309   -.1380664
       _cons |   1.104814   .6555608     1.69   0.094    -.1890086    2.398637
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000
Warning: 100% of the resampled realizations for _pm_1 are exactly identical to
>  original value
Warning: 100% of the resampled realizations for _pm_2 are exactly identical to
>  original value

      Command: regress fdamt i.distXtrXdateFes fYes_T mage mmarried makan
                   mselfemployed m2q1a i.m3q1 trt2 trt3 trt4
        _pm_1: trt3
        _pm_2: trt4
        _pm_3: _b[trt2]
        _pm_4: _b[trt3]
        _pm_5: _b[trt4]
  res. var(s):  trt2
   Resampling:  Permuting trt2
Clust. var(s):  uniqueVendorID
     Clusters:  207
Strata var(s):  ge01
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_2 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_3 |  -.4390063      38    1000  0.0380  0.0060  .0270288   .0517871
       _pm_4 |  -.5748369       0    1000  0.0000  0.0000         0   .0036821
       _pm_5 |  -.5545986       0    1000  0.0000  0.0000         0   .0036821
------------------------------------------------------------------------------
Note: Confidence intervals are with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. ritest trt2 trt3 trt4 _b[trt2] _b[trt3] _b[trt4], reps($bootstrap_reps) clus
> ter(uniqueVendorID) strata(ge01) seed(546): reg ihs_fdamt i.distXtrXdateFes 
> fYes_T mage mmarried makan mselfemployed m2q1a i.m3q1 trt2 trt3 trt4
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       335
-------------+----------------------------------   F(159, 175)     =      2.18
       Model |  91.1778899       159  .573445848   Prob > F        =    0.0000
    Residual |  45.9356542       175  .262489453   R-squared       =    0.6650
-------------+----------------------------------   Adj R-squared   =    0.3606
       Total |  137.113544       334  .410519593   Root MSE        =    .51234

------------------------------------------------------------------------------
   ihs_fdamt | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.2564068   .6042339    -0.42   0.672     -1.44893    .9361168
          5  |   .9801978   .4408547     2.22   0.027     .1101214    1.850274
          6  |  -.1505601   .6263918    -0.24   0.810    -1.386815    1.085695
          7  |  -.2564068   .6042339    -0.42   0.672     -1.44893    .9361168
          8  |   .0018181   .4019215     0.00   0.996    -.7914192    .7950555
         11  |   .0175204    .609592     0.03   0.977    -1.185578    1.220619
         14  |   .0175204    .609592     0.03   0.977    -1.185578    1.220619
         18  |  -.1505601   .6263918    -0.24   0.810    -1.386815    1.085695
         22  |  -.2564068   .6042339    -0.42   0.672     -1.44893    .9361168
         23  |  -.0144079   .4279706    -0.03   0.973    -.8590559    .8302401
         24  |  -.1505601   .6263918    -0.24   0.810    -1.386815    1.085695
         26  |  -.1357225   .3953699    -0.34   0.732    -.9160294    .6445844
         27  |  -.1505601   .6263918    -0.24   0.810    -1.386815    1.085695
         29  |  -.1413738   .4036618    -0.35   0.727    -.9380457    .6552982
         30  |  -.1505601   .6263918    -0.24   0.810    -1.386815    1.085695
         32  |   .0551518   .3776461     0.15   0.884    -.6901752    .8004788
         33  |  -.1505601   .6263918    -0.24   0.810    -1.386815    1.085695
         34  |  -.2564068   .6042339    -0.42   0.672     -1.44893    .9361168
         35  |  -.0627611   .4331168    -0.14   0.885    -.9175657    .7920436
         37  |  -.1559539   .3526621    -0.44   0.659    -.8519723    .5400645
         38  |   .0801441   .3691158     0.22   0.828    -.6483476    .8086357
         39  |  -.1563142    .424041    -0.37   0.713    -.9932068    .6805785
         40  |   .4583602   .3630099     1.26   0.208    -.2580807    1.174801
         41  |  -.0566842   .3724975    -0.15   0.879      -.79185    .6784816
         42  |   .1163507     .41052     0.28   0.777    -.6938567     .926558
         51  |    -.17421   .4232214    -0.41   0.681    -1.009485    .6610651
         52  |  -.3578023   .4342389    -0.82   0.411    -1.214822     .499217
         53  |   -.183867   .4306168    -0.43   0.670    -1.033738    .6660037
         54  |  -.0692402   .4368003    -0.16   0.874    -.9313148    .7928343
         55  |  -.1402076   .3977479    -0.35   0.725    -.9252077    .6447926
         56  |  -.0115272    .434075    -0.03   0.979    -.8682231    .8451687
         57  |    1.01254   .4833886     2.09   0.038     .0585183    1.966562
         58  |   .5954099   .4787712     1.24   0.215    -.3494989    1.540319
         61  |  -.1238538   .4338568    -0.29   0.776    -.9801189    .7324113
         62  |   -.198671   .4777004    -0.42   0.678    -1.141466    .7441245
         63  |  -.2229294   .4353143    -0.51   0.609    -1.082071    .6362122
         64  |  -.4257171   .6063412    -0.70   0.484      -1.6224    .7709655
         65  |   .0181176   .4306303     0.04   0.966    -.8317797    .8680149
         66  |   1.850705   .4847782     3.82   0.000     .8939407    2.807469
         67  |   1.046888   .3899474     2.68   0.008     .2772827    1.816493
         68  |  -.4257171   .6063412    -0.70   0.484      -1.6224    .7709655
         69  |   .1103154   .6082829     0.18   0.856    -1.090199     1.31083
         71  |   .1957817   .3841803     0.51   0.611    -.5624413    .9540048
         72  |  -.4257171   .6063412    -0.70   0.484      -1.6224    .7709655
         89  |   .0653957   .4259287     0.15   0.878    -.7752225     .906014
         90  |  -.1908848   .6037645    -0.32   0.752    -1.382482    1.000712
         91  |   -.317212   .4436478    -0.72   0.476    -1.192801    .5583767
         93  |    .099499   .4044963     0.25   0.806      -.69882    .8978179
         94  |  -.3305771   .4896717    -0.68   0.501    -1.296999     .635845
         95  |  -.3120638   .4150709    -0.75   0.453    -1.131253    .5071253
         97  |  -.0509256   .4039112    -0.13   0.900    -.8480898    .7462386
         98  |   -.198671   .4777004    -0.42   0.678    -1.141466    .7441245
         99  |  -.2470945   .4146637    -0.60   0.552     -1.06548     .571291
        102  |   .2754626    .477818     0.58   0.565     -.667565     1.21849
        103  |   .9543284   .4789662     1.99   0.048     .0090348    1.899622
        104  |   .2825175   .6180881     0.46   0.648    -.9373489    1.502384
        107  |  -.0107994   .4351701    -0.02   0.980    -.8696564    .8480577
        109  |  -.3063068   .4769218    -0.64   0.522    -1.247566    .6349519
        110  |  -.1360146   .4028799    -0.34   0.736    -.9311433    .6591141
        113  |  -.1525082   .5980545    -0.26   0.799    -1.332836     1.02782
        114  |  -.3047896   .4785644    -0.64   0.525     -1.24929     .639711
        117  |  -.1525082   .5980545    -0.26   0.799    -1.332836     1.02782
        118  |  -.3271514   .4013922    -0.82   0.416    -1.119344    .4650413
        137  |  -.3256472    .606538    -0.54   0.592    -1.522718    .8714238
        138  |  -.2617407   .4312856    -0.61   0.545    -1.112931      .58945
        141  |  -.4601055   .6102203    -0.75   0.452    -1.664444    .7442329
        142  |   -.296401   .6039044    -0.49   0.624    -1.488274    .8954723
        145  |  -.2869665   .6019729    -0.48   0.634    -1.475028    .9010947
        146  |  -.2591942   .4334825    -0.60   0.551    -1.114721    .5963323
        149  |   .5020804   .4743285     1.06   0.291    -.4340602    1.438221
        150  |   .6273444   .4262046     1.47   0.143    -.2138182    1.468507
        153  |  -.1525082   .5980545    -0.26   0.799    -1.332836     1.02782
        154  |  -.0795188   .6087498    -0.13   0.896    -1.280955    1.121917
        157  |  -.1107266   .4774974    -0.23   0.817    -1.053121    .8316682
        158  |  -.0110625   .4933728    -0.02   0.982    -.9847893    .9626643
        159  |   .0089598   .5986279     0.01   0.988      -1.1725    1.190419
        160  |   1.921319   .6170153     3.11   0.002     .7035696    3.139068
        162  |   .7079798   .6170153     1.15   0.253    -.5097693    1.925729
        171  |  -.2304129   .6120898    -0.38   0.707    -1.438441    .9776152
        172  |   .1512688   .6159054     0.25   0.806     -1.06429    1.366827
        173  |  -.2304129   .6120898    -0.38   0.707    -1.438441    .9776152
        174  |  -.1733938   .6170153    -0.28   0.779    -1.391143    1.044355
        175  |  -.1107266   .4774974    -0.23   0.817    -1.053121    .8316682
        176  |  -.1733938   .6170153    -0.28   0.779    -1.391143    1.044355
        177  |   .7171943   .6065185     1.18   0.239    -.4798381    1.914227
        178  |   .4296243   .4933728     0.87   0.385    -.5441025    1.403351
        180  |   .8981608   .4933728     1.82   0.070     -.075566    1.871888
        181  |  -.3917694   .4797315    -0.82   0.415    -1.338573    .5550346
        182  |  -.0938236   .4318484    -0.22   0.828     -.946125    .7584778
        183  |   1.857141   .6024916     3.08   0.002     .6680563    3.046226
        184  |   .4888672   .4400258     1.11   0.268    -.3795733    1.357308
        185  |  -.4088319   .6117007    -0.67   0.505    -1.616092    .7984282
        186  |  -.0703179    .482412    -0.15   0.884    -1.022412    .8817764
        187  |  -.2375712   .6024916    -0.39   0.694    -1.426656    .9515138
        189  |  -.2375712   .6024916    -0.39   0.694    -1.426656    .9515138
        191  |  -.2375712   .6024916    -0.39   0.694    -1.426656    .9515138
        193  |  -.2375712   .6024916    -0.39   0.694    -1.426656    .9515138
        195  |   -.306139   .4807404    -0.64   0.525    -1.254934    .6426562
        196  |   1.053771   .4789401     2.20   0.029      .108529    1.999013
        197  |   .0111342   .4271794     0.03   0.979    -.8319524    .8542208
        198  |  -.2307893   .6096655    -0.38   0.705    -1.434033    .9724542
        199  |   -.282657   .4271794    -0.66   0.509    -1.125744    .5604296
        200  |   -.128065   .4061212    -0.32   0.753     -.929591    .6734609
        201  |   1.495245   .4790128     3.12   0.002     .5498594    2.440631
        202  |   2.254788   .5997139     3.76   0.000     1.071185    3.438391
        203  |   2.074867   .6024916     3.44   0.001     .8857821    3.263952
        204  |   -.140835   .6072661    -0.23   0.817    -1.339343    1.057673
        205  |    -.11519   .3814604    -0.30   0.763     -.868045    .6376651
        206  |    .967335   .4832524     2.00   0.047     .0135821    1.921088
        207  |   .4211473   .4081559     1.03   0.304    -.3843943    1.226689
        210  |  -.1410476   .4247829    -0.33   0.740    -.9794044    .6973091
        211  |  -.2564736   .4302406    -0.60   0.552    -1.105602    .5926546
        212  |  -.2023455   .6021193    -0.34   0.737    -1.390696    .9860046
        213  |  -.1749241   .4343612    -0.40   0.688    -1.032185    .6823366
        214  |  -.2854665   .4934176    -0.58   0.564    -1.259282    .6883486
        215  |    .679028   .6021193     1.13   0.261    -.5093221    1.867378
        216  |   .7968957   .4817604     1.65   0.100    -.1539126    1.747704
        217  |   .6982033   .4361745     1.60   0.111    -.1626361    1.559043
        219  |   .7681745   .4214266     1.82   0.070    -.0635583    1.599907
        221  |   .4358363   .4302231     1.01   0.312    -.4132572     1.28493
        227  |   -.179868   .6152719    -0.29   0.770    -1.394176     1.03444
        231  |  -.1303224   .4410601    -0.30   0.768    -1.000804    .7401592
        233  |  -.0768013   .4345908    -0.18   0.860    -.9345152    .7809126
        235  |  -.1738101   .4112437    -0.42   0.673    -.9854458    .6378256
        237  |   1.253647   .4880407     2.57   0.011      .290444     2.21685
        239  |   2.378693   .4807106     4.95   0.000     1.429957     3.32743
        241  |  -.0858361   .6017851    -0.14   0.887    -1.273527    1.101854
        243  |  -.4778774   .6047034    -0.79   0.430    -1.671328    .7155728
        244  |  -.1607287   .4720836    -0.34   0.734    -1.092439    .7709813
        245  |  -.0858361   .6017851    -0.14   0.887    -1.273527    1.101854
        247  |   1.095891   .4296027     2.55   0.012     .2480218     1.94376
        248  |  -.2065414   .4756617    -0.43   0.665    -1.145313    .7322303
        250  |  -.2466862   .6086149    -0.41   0.686    -1.447856    .9544837
        251  |  -.1028593   .4369651    -0.24   0.814    -.9652591    .7595405
        252  |   -.170457   .5994233    -0.28   0.776    -1.353486    1.012572
        267  |  -.2035184   .5998142    -0.34   0.735    -1.387319    .9802824
        269  |  -.0858361   .6017851    -0.14   0.887    -1.273527    1.101854
        271  |  -.4932165   .4949027    -1.00   0.320    -1.469963    .4835295
        272  |  -.2519706   .6089371    -0.41   0.680    -1.453776    .9498353
        274  |  -.2466862   .6086149    -0.41   0.686    -1.447856    .9544837
        275  |    -.31081    .436044    -0.71   0.477    -1.171392    .5497718
        276  |  -.1607287   .4720836    -0.34   0.734    -1.092439    .7709813
        278  |  -.2466862   .6086149    -0.41   0.686    -1.447856    .9544837
        279  |  -.2612356   .4850686    -0.54   0.591    -1.218573    .6961018
        280  |   -.170457   .5994233    -0.28   0.776    -1.353486    1.012572
        283  |  -.0880967    .473942    -0.19   0.853    -1.023474    .8472812
        284  |   .5377776    .608645     0.88   0.378    -.6634519    1.739007
        285  |  -.2589751   .6122978    -0.42   0.673    -1.467414    .9494635
        287  |  -.2035184   .5998142    -0.34   0.735    -1.387319    .9802824
             |
      fYes_T |    .173139   .1017308     1.70   0.091    -.0276381    .3739161
        mage |  -.0066695   .0057674    -1.16   0.249    -.0180521    .0047131
    mmarried |   .0552353   .1032351     0.54   0.593    -.1485109    .2589814
       makan |   -.096501   .0930989    -1.04   0.301    -.2802422    .0872402
mselfemplo~d |  -.1681374   .0800934    -2.10   0.037    -.3262108   -.0100641
       m2q1a |   .0255051   .0230811     1.11   0.271    -.0200481    .0710583
      2.m3q1 |  -.0575079   .1171274    -0.49   0.624    -.2886719    .1736561
        trt2 |  -.2485905   .1358564    -1.83   0.069    -.5167184    .0195373
        trt3 |  -.3413888    .112532    -3.03   0.003    -.5634833   -.1192944
        trt4 |  -.3253692   .1094626    -2.97   0.003     -.541406   -.1093324
       _cons |   .6179364   .3400103     1.82   0.071    -.0531121    1.288985
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000
Warning: 100% of the resampled realizations for _pm_1 are exactly identical to
>  original value
Warning: 100% of the resampled realizations for _pm_2 are exactly identical to
>  original value

      Command: regress ihs_fdamt i.distXtrXdateFes fYes_T mage mmarried
                   makan mselfemployed m2q1a i.m3q1 trt2 trt3 trt4
        _pm_1: trt3
        _pm_2: trt4
        _pm_3: _b[trt2]
        _pm_4: _b[trt3]
        _pm_5: _b[trt4]
  res. var(s):  trt2
   Resampling:  Permuting trt2
Clust. var(s):  uniqueVendorID
     Clusters:  207
Strata var(s):  ge01
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_2 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_3 |  -.2485905      26    1000  0.0260  0.0050  .0170528   .0378651
       _pm_4 |  -.3413888       0    1000  0.0000  0.0000         0   .0036821
       _pm_5 |  -.3253692       0    1000  0.0000  0.0000         0   .0036821
------------------------------------------------------------------------------
Note: Confidence intervals are with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. *mht: implement Romano-Wolf (2005) procedure, pval
. rwolf fd fdamt ihs_fdamt, indepvar(trt2 trt3 trt4) reps($bootstrap_reps) see
> d(124) controls(i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m
> 2q1a i.m3q1) //family (misconduct: 0/1, amount)
Bootstrap replications (1000). This may take some time.
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..................................................     50
..................................................     100
..................................................     150
..................................................     200
..................................................     250
..................................................     300
..................................................     350
..................................................     400
..................................................     450
..................................................     500
..................................................     550
..................................................     600
..................................................     650
..................................................     700
..................................................     750
..................................................     800
..................................................     850
..................................................     900
..................................................     950
..................................................     1000




Romano-Wolf step-down adjusted p-values


Independent variable:  trt2
Outcome variables:   fd fdamt ihs_fdamt
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
                 fd |     0.0182             0.0959              0.1109
              fdamt |     0.0955             0.1628              0.1628
          ihs_fdamt |     0.0690             0.1419              0.1568
------------------------------------------------------------------------------


Independent variable:  trt3
Outcome variables:   fd fdamt ihs_fdamt
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
                 fd |     0.0009             0.0380              0.0480
              fdamt |     0.0088             0.0460              0.0480
          ihs_fdamt |     0.0028             0.0310              0.0480
------------------------------------------------------------------------------


Independent variable:  trt4
Outcome variables:   fd fdamt ihs_fdamt
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
                 fd |     0.0009             0.0380              0.0440
              fdamt |     0.0094             0.0619              0.0619
          ihs_fdamt |     0.0034             0.0400              0.0480
------------------------------------------------------------------------------



. *attrition bounds
. leebounds fd trt2, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   2307
Number of selected obs.            =   1149
Trimming porportion                =   0.0145
Effect 95% conf. interval          : [-0.1295  0.0175]

------------------------------------------------------------------------------
          fd | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt2         |
       lower |  -.0839613    .024888    -3.37   0.001    -.1327409   -.0351817
       upper |   -.069207   .0474137    -1.46   0.144    -.1621361     .023722
------------------------------------------------------------------------------

. leebounds fd trt3, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   2307
Number of selected obs.            =   1149
Trimming porportion                =   0.0639
Effect 95% conf. interval          : [-0.2149  -0.0240]

------------------------------------------------------------------------------
          fd | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt3         |
       lower |  -.1323289   .0498581    -2.65   0.008    -.2300491   -.0346088
       upper |  -.0641043   .0241826    -2.65   0.008    -.1115014   -.0167072
------------------------------------------------------------------------------

. leebounds fd trt4, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   2307
Number of selected obs.            =   1149
Trimming porportion                =   0.0601
Effect 95% conf. interval          : [-0.0735  0.1170]

------------------------------------------------------------------------------
          fd | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt4         |
       lower |  -.0293471   .0265609    -1.10   0.269    -.0814055    .0227114
       upper |   .0345815   .0495967     0.70   0.486    -.0626262    .1317892
------------------------------------------------------------------------------

. 
. leebounds fdamt trt2, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   2307
Number of selected obs.            =   1149
Trimming porportion                =   0.0145
Effect 95% conf. interval          : [-0.4164  0.2187]

------------------------------------------------------------------------------
       fdamt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt2         |
       lower |  -.2950019    .066842    -4.41   0.000    -.4260099    -.163994
       upper |  -.2132312   .2377329    -0.90   0.370    -.6791792    .2527168
------------------------------------------------------------------------------

. leebounds fdamt trt3, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   2307
Number of selected obs.            =   1149
Trimming porportion                =   0.0639
Effect 95% conf. interval          : [-0.5131  0.0022]

------------------------------------------------------------------------------
       fdamt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt3         |
       lower |  -.4093801   .0630476    -6.49   0.000    -.5329511    -.285809
       upper |  -.1313256   .0811939    -1.62   0.106    -.2904628    .0278116
------------------------------------------------------------------------------

. leebounds fdamt trt4, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   2307
Number of selected obs.            =   1149
Trimming porportion                =   0.0601
Effect 95% conf. interval          : [-0.2057  0.5498]

------------------------------------------------------------------------------
       fdamt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt4         |
       lower |  -.0704992   .0816992    -0.86   0.388    -.2306267    .0896282
       upper |   .2176062   .2007417     1.08   0.278    -.1758403    .6110527
------------------------------------------------------------------------------

. 
. leebounds ihs_fdamt trt2, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   2307
Number of selected obs.            =   1149
Trimming porportion                =   0.0145
Effect 95% conf. interval          : [-0.2238  0.0639]

------------------------------------------------------------------------------
   ihs_fdamt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt2         |
       lower |  -.1525892   .0371649    -4.11   0.000     -.225431   -.0797474
       upper |  -.1427128   .1078428    -1.32   0.186    -.3540808    .0686551
------------------------------------------------------------------------------

. leebounds ihs_fdamt trt3, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   2307
Number of selected obs.            =   1149
Trimming porportion                =   0.0639
Effect 95% conf. interval          : [-0.3060  -0.0254]

------------------------------------------------------------------------------
   ihs_fdamt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt3         |
       lower |  -.2276552   .0476101    -4.78   0.000    -.3209693   -.1343411
       upper |  -.0909464   .0398792    -2.28   0.023    -.1691082   -.0127846
------------------------------------------------------------------------------

. leebounds ihs_fdamt trt4, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   2307
Number of selected obs.            =   1149
Trimming porportion                =   0.0601
Effect 95% conf. interval          : [-0.1144  0.2692]

------------------------------------------------------------------------------
   ihs_fdamt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt4         |
       lower |   -.043003   .0430593    -1.00   0.318    -.1273977    .0413917
       upper |   .0973302   .1036484     0.94   0.348    -.1058171    .3004774
------------------------------------------------------------------------------

. 
. 
. 
. 
. ** Table C.8 ---------------------------------------------------------------
> ----
. *Robustness checks [SPILLOVER EFFECTS] - Inference, Multiple Testing, Attrit
> ion, LASSO Estimation
. *UNTREATED VENDORS
. *preserve
. *POOLED
. keep if _merge==1
(1,548 observations deleted)

. *wild cluster bootstrap, pval
. reg fd i.distXtrXdateFes trt, r cluster(uniqueLocalityID) level(95)

Linear regression                               Number of obs     =        411
                                                F(24, 62)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.7148
                                                Root MSE          =     .25394

                      (Std. err. adjusted for 63 clusters in uniqueLocalityID)
------------------------------------------------------------------------------
             |               Robust
          fd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          2  |   .1698041   .0675586     2.51   0.015     .0347565    .3048517
          3  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
          4  |  -5.83e-15   1.11e-07    -0.00   1.000    -2.22e-07    2.22e-07
          5  |   .2809152   .1448734     1.94   0.057    -.0086826     .570513
          6  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
          8  |   .2809152   .1448734     1.94   0.057    -.0086826     .570513
          9  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         14  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         17  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         18  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         20  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         21  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         22  |  -5.82e-15   1.11e-07    -0.00   1.000    -2.22e-07    2.22e-07
         23  |   .1698041   .0675586     2.51   0.015     .0347565    .3048517
         24  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         25  |  -5.80e-15   9.25e-08    -0.00   1.000    -1.85e-07    1.85e-07
         26  |   .1698041   .0675586     2.51   0.015     .0347565    .3048517
         27  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         28  |  -5.83e-15   9.25e-08    -0.00   1.000    -1.85e-07    1.85e-07
         29  |   .1698041   .0675586     2.51   0.015     .0347565    .3048517
         30  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         32  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         35  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         36  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         37  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         38  |   .4137397   .2476336     1.67   0.100    -.0812729    .9087522
         39  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         40  |   .4137397   .2476336     1.67   0.100    -.0812729    .9087522
         41  |   .5516529   .3629716     1.52   0.134     -.173917    1.277223
         42  |   .4137397   .2476336     1.67   0.100    -.0812729    .9087522
         48  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
         50  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
         51  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         52  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         53  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         54  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         55  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         56  |   .1637397   .0594339     2.75   0.008      .044933    .2825463
         57  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
         58  |    1.10916   .0940016    11.80   0.000     .9212533    1.297066
         61  |   .7183196   .4684223     1.53   0.130    -.2180433    1.654682
         62  |   .1455464   .0946645     1.54   0.129    -.0436852    .3347779
         63  |   .1091598   .0773485     1.41   0.163    -.0454576    .2637772
         64  |  -5.86e-15   9.25e-08    -0.00   1.000    -1.85e-07    1.85e-07
         65  |   .7183196   .4684223     1.53   0.130    -.2180433    1.654682
         66  |   .1455464   .0946645     1.54   0.129    -.0436852    .3347779
         67  |   .6091598    .259856     2.34   0.022     .0897151    1.128604
         68  |  -5.82e-15   9.25e-08    -0.00   1.000    -1.85e-07    1.85e-07
         69  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
         71  |   .6091598    .259856     2.34   0.022     .0897151    1.128604
         72  |  -5.83e-15   9.25e-08    -0.00   1.000    -1.85e-07    1.85e-07
         74  |          1   9.25e-08  1.1e+07   0.000     .9999998           1
         82  |  -5.84e-15   9.25e-08    -0.00   1.000    -1.85e-07    1.85e-07
         89  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         90  |   .1455464   .0946645     1.54   0.129    -.0436852    .3347779
         91  |   .1091598   .0773485     1.41   0.163    -.0454576    .2637772
         92  |  -5.81e-15   9.25e-08    -0.00   1.000    -1.85e-07    1.85e-07
         93  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         94  |   .1455464   .0946645     1.54   0.129    -.0436852    .3347779
         95  |   .1091598   .0773485     1.41   0.163    -.0454576    .2637772
         96  |  -5.80e-15   9.25e-08    -0.00   1.000    -1.85e-07    1.85e-07
         97  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
         98  |   .1455464   .0946645     1.54   0.129    -.0436852    .3347779
         99  |   .1091598   .0773485     1.41   0.163    -.0454576    .2637772
        100  |  -5.84e-15   9.25e-08    -0.00   1.000    -1.85e-07    1.85e-07
        101  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
        102  |          1   9.25e-08  1.1e+07   0.000     .9999998           1
        103  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
        104  |          1   9.25e-08  1.1e+07   0.000     .9999998           1
        105  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
        107  |   .4788797   .2870073     1.67   0.100    -.0948397    1.052599
        108  |  -5.80e-15   9.25e-08    -0.00   1.000    -1.85e-07    1.85e-07
        109  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        110  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        111  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        112  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        113  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        114  |   .8849862   .3629716     2.44   0.018     .1594163    1.610556
        115  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        116  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        117  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        118  |   .8849862   .3629716     2.44   0.018     .1594163    1.610556
        119  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        120  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        137  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        138  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        139  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        140  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        141  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        142  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        143  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        144  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        145  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        146  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        147  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        148  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        149  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
        150  |   .7183196   .4684223     1.53   0.130    -.2180433    1.654682
        151  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
        152  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
        154  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        157  |  -5.85e-15   9.25e-08    -0.00   1.000    -1.85e-07    1.85e-07
        158  |   .1455464    .089287     1.63   0.108    -.0329357    .3240285
        159  |  -5.84e-15   9.26e-08    -0.00   1.000    -1.85e-07    1.85e-07
        160  |   .4788797   .2823863     1.70   0.095    -.0856026    1.043362
        161  |  -5.83e-15   9.35e-08    -0.00   1.000    -1.87e-07    1.87e-07
        162  |   .1455464    .089287     1.63   0.108    -.0329357    .3240285
        171  |   .1091598   .1063651     1.03   0.309     -.103461    .3217805
        172  |   .1455464    .089287     1.63   0.108    -.0329357    .3240285
        173  |  -5.84e-15   9.34e-08    -0.00   1.000    -1.87e-07    1.87e-07
        174  |   .1455464    .089287     1.63   0.108    -.0329357    .3240285
        175  |   .1091598   .1063651     1.03   0.309     -.103461    .3217805
        176  |   .1455464    .089287     1.63   0.108    -.0329357    .3240285
        177  |          1   9.31e-08  1.1e+07   0.000     .9999998           1
        178  |   .6091598   .3628815     1.68   0.098    -.1162299    1.334549
        179  |          1   6.77e-08  1.5e+07   0.000     .9999999           1
        182  |   .5516529   .3629716     1.52   0.134     -.173917    1.277223
        184  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        186  |   .5516529   .3629716     1.52   0.134     -.173917    1.277223
        196  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        198  |   .5516529   .3629716     1.52   0.134     -.173917    1.277223
        200  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        202  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
        205  |   .1637397   .0840645     1.95   0.056    -.0043028    .3317821
        206  |   .9137397   .2634322     3.47   0.001     .3871462    1.440333
        207  |   .9137397   .2634322     3.47   0.001     .3871462    1.440333
        212  |   .1637397   .0840645     1.95   0.056    -.0043028    .3317821
        213  |   .1637397   .0840645     1.95   0.056    -.0043028    .3317821
        214  |   .1637397   .0840645     1.95   0.056    -.0043028    .3317821
        215  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
        217  |   .1455464   .0922019     1.58   0.120    -.0387626    .3298553
        218  |  -5.82e-15   6.83e-08    -0.00   1.000    -1.37e-07    1.37e-07
        219  |    .812213   .4406785     1.84   0.070    -.0686908    1.693117
        220  |          1   6.83e-08  1.5e+07   0.000     .9999999           1
        221  |    .812213   .3221391     2.52   0.014     .1682661     1.45616
        222  |          1   6.81e-08  1.5e+07   0.000     .9999999           1
        227  |  -5.89e-15   6.83e-08    -0.00   1.000    -1.37e-07    1.37e-07
        231  |   .1455464   .0922019     1.58   0.120    -.0387626    .3298553
        232  |  -5.80e-15   6.83e-08    -0.00   1.000    -1.37e-07    1.37e-07
        233  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        234  |  -5.86e-15   6.81e-08    -0.00   1.000    -1.36e-07    1.36e-07
        235  |   .4788797   .2790124     1.72   0.091    -.0788581    1.036618
        236  |  -5.81e-15   6.83e-08    -0.00   1.000    -1.37e-07    1.37e-07
        237  |          1   6.61e-08  1.5e+07   0.000     .9999999           1
        239  |          1   6.69e-08  1.5e+07   0.000     .9999999           1
        242  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        243  |   .1091598   .1056297     1.03   0.305     -.101991    .3203105
        244  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        246  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        247  |   .4061065   .3285092     1.24   0.221    -.2505741    1.062787
        248  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        250  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        251  |   .1091598   .1025097     1.06   0.291    -.0957542    .3140738
        252  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        270  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        271  |   .0727732   .0791389     0.92   0.361    -.0854233    .2309696
        272  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        274  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        275  |   .0727732   .0791389     0.92   0.361    -.0854233    .2309696
        276  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        278  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        279  |   .0727732   .0791389     0.92   0.361    -.0854233    .2309696
        280  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
        282  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
        283  |   .1091598   .1025097     1.06   0.291    -.0957542    .3140738
        284  |    1.21832   .0650142    18.74   0.000     1.088358    1.348281
        288  |   .2183196   .0650142     3.36   0.001      .088358    .3482811
             |
         trt |  -.2183196   .0650142    -3.36   0.001    -.3482811    -.088358
       _cons |   5.86e-15   7.21e-08     0.00   1.000    -1.44e-07    1.44e-07
------------------------------------------------------------------------------

. boottest trt, rep($bootstrap_reps) level(95) nogr

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueLocalityID, Rademacher weights:
  trt

                           t(62) =    -3.3580
                        Prob>|t| =     0.0010

95% confidence set for null hypothesis expression: [−.351, −.08009]

. reg fdamt i.distXtrXdateFes trt, r cluster(uniqueLocalityID) level(95)

Linear regression                               Number of obs     =        411
                                                F(24, 62)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6884
                                                Root MSE          =      .8773

                      (Std. err. adjusted for 63 clusters in uniqueLocalityID)
------------------------------------------------------------------------------
             |               Robust
       fdamt | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          2  |   .5040557   .2129417     2.37   0.021     .0783914    .9297201
          3  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
          4  |  -1.23e-14          .        .       .            .           .
          5  |   .9485002   .5491709     1.73   0.089    -.1492769    2.046277
          6  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
          8  |   .6151668   .2296281     2.68   0.009     .1561469    1.074187
          9  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         14  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         17  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         18  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         20  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         21  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         22  |  -1.23e-14          .        .       .            .           .
         23  |   .5040557   .2129417     2.37   0.021     .0783914    .9297201
         24  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         25  |  -1.23e-14          .        .       .            .           .
         26  |   .5040557   .2129417     2.37   0.021     .0783914    .9297201
         27  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         28  |  -1.24e-14          .        .       .            .           .
         29  |   .5040557   .2129417     2.37   0.021     .0783914    .9297201
         30  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         32  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         35  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         36  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         37  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         38  |   1.736054   1.325721     1.31   0.195    -.9140252    4.386133
         39  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         40  |   1.486054   1.043403     1.42   0.159    -.5996782    3.571786
         41  |    .981405   .4123609     2.38   0.020     .1571072    1.805703
         42  |   .7360537   .2247485     3.28   0.002     .2867879     1.18532
         48  |   5.648072   .2062037    27.39   0.000     5.235876    6.060267
         50  |   10.64807   .2062037    51.64   0.000     10.23588    11.06027
         51  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         52  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         53  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         54  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         55  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         56  |   .4860537    .165051     2.94   0.005     .1561215    .8159859
         57  |   1.648072   .2062037     7.99   0.000     1.235876    2.060267
         58  |   2.324036   .6762926     3.44   0.001     .9721461    3.675926
         61  |   3.148072   2.328591     1.35   0.181    -1.506715    7.802858
         62  |   .4320478   .2851475     1.52   0.135    -.1379539    1.002049
         63  |   .3240358   .2324068     1.39   0.168    -.1405388    .7886105
         64  |  -1.23e-14          .        .       .            .           .
         65  |   2.648072   1.866977     1.42   0.161    -1.083962    6.380105
         66  |   .4320478   .2851475     1.52   0.135    -.1379539    1.002049
         67  |   2.324036   1.108543     2.10   0.040     .1080904    4.539981
         68  |  -1.22e-14          .        .       .            .           .
         69  |   1.648072   .2062037     7.99   0.000     1.235876    2.060267
         71  |   .8240358   .1579163     5.22   0.000     .5083657    1.139706
         72  |  -1.23e-14          .        .       .            .           .
         74  |          5          .        .       .            .           .
         82  |  -1.23e-14          .        .       .            .           .
         89  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         90  |   .4320478   .2851475     1.52   0.135    -.1379539    1.002049
         91  |   .3240358   .2324068     1.39   0.168    -.1405388    .7886105
         92  |  -1.23e-14          .        .       .            .           .
         93  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         94  |   .4320478   .2851475     1.52   0.135    -.1379539    1.002049
         95  |   .3240358   .2324068     1.39   0.168    -.1405388    .7886105
         96  |  -1.22e-14          .        .       .            .           .
         97  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
         98  |   .4320478   .2851475     1.52   0.135    -.1379539    1.002049
         99  |   .3240358   .2324068     1.39   0.168    -.1405388    .7886105
        100  |  -1.24e-14          .        .       .            .           .
        101  |   1.648072   .2062037     7.99   0.000     1.235876    2.060267
        102  |          1          .        .       .            .           .
        103  |   1.648072   .2062037     7.99   0.000     1.235876    2.060267
        104  |          1          .        .       .            .           .
        105  |   3.648072   .2062037    17.69   0.000     3.235876    4.060267
        107  |   1.432048   .8646448     1.66   0.103    -.2963525    3.160448
        108  |  -1.22e-14          .        .       .            .           .
        109  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        110  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        111  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        112  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        113  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        114  |   3.314738   1.443213     2.30   0.025     .4297959    6.199681
        115  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        116  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        117  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        118  |   1.314738   .4123609     3.19   0.002     .4904406    2.139036
        119  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        120  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        137  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        138  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        139  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        140  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        141  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        142  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        143  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        144  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        145  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        146  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        147  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        148  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        149  |   1.648072   .2062037     7.99   0.000     1.235876    2.060267
        150  |   1.148072    .507654     2.26   0.027     .1332857    2.162858
        151  |   1.648072   .2062037     7.99   0.000     1.235876    2.060267
        152  |   1.648072   .2062037     7.99   0.000     1.235876    2.060267
        154  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        157  |  -1.23e-14          .        .       .            .           .
        158  |   .4320478   .2679385     1.61   0.112    -.1035537    .9676492
        159  |  -1.23e-14          .        .       .            .           .
        160  |   1.765381   1.206274     1.46   0.148     -.645927    4.176689
        161  |  -1.22e-14          .        .       .            .           .
        162  |   .4320478   .2679385     1.61   0.112    -.1035537    .9676492
        171  |   .3240358    .317821     1.02   0.312    -.3112794     .959351
        172  |   .4320478   .2679385     1.61   0.112    -.1035537    .9676492
        173  |  -1.24e-14          .        .       .            .           .
        174  |   .4320478   .2679385     1.61   0.112    -.1035537    .9676492
        175  |   .3240358    .317821     1.02   0.312    -.3112794     .959351
        176  |   .4320478   .2679385     1.61   0.112    -.1035537    .9676492
        177  |          1          .        .       .            .           .
        178  |   .8240358   .1934477     4.26   0.000     .4373393    1.210732
        179  |          5          .        .       .            .           .
        182  |   2.314738   1.797376     1.29   0.203    -1.278164    5.907641
        184  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        186  |    .981405   .4123609     2.38   0.020     .1571072    1.805703
        196  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        198  |   2.314738   1.797376     1.29   0.203    -1.278164    5.907641
        200  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        202  |   3.648072   .2062037    17.69   0.000     3.235876    4.060267
        205  |   .4860537   .2577838     1.89   0.064    -.0292489    1.001356
        206  |   3.486054   1.070987     3.25   0.002     1.345181    5.626927
        207  |   1.236054   .3074103     4.02   0.000     .6215493    1.850558
        212  |   .4860537   .2577838     1.89   0.064    -.0292489    1.001356
        213  |   .4860537   .2577838     1.89   0.064    -.0292489    1.001356
        214  |   .4860537   .2577838     1.89   0.064    -.0292489    1.001356
        215  |   5.648072   .2062037    27.39   0.000     5.235876    6.060267
        217  |   .4320478   .2778688     1.55   0.125    -.1234041    .9874996
        218  |  -1.22e-14          .        .       .            .           .
        219  |   3.098714   1.675723     1.85   0.069    -.2510079    6.448437
        220  |          4          .        .       .            .           .
        221  |   1.098714   .3394989     3.24   0.002     .4200657    1.777363
        222  |          1          .        .       .            .           .
        227  |  -1.24e-14          .        .       .            .           .
        231  |   .4320478   .2778688     1.55   0.125    -.1234041    .9874996
        232  |  -1.22e-14          .        .       .            .           .
        233  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        234  |  -1.23e-14          .        .       .            .           .
        235  |   2.098714   1.549882     1.35   0.181    -.9994565    5.196885
        236  |  -1.22e-14          .        .       .            .           .
        237  |          3          .        .       .            .           .
        239  |          4          .        .       .            .           .
        242  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        243  |   .3240358   .3193968     1.01   0.314    -.3144294     .962501
        244  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        246  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        247  |   1.549357   1.339418     1.16   0.252    -1.128101    4.226816
        248  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        250  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        251  |   .3240358   .3064543     1.06   0.294    -.2885576    .9366292
        252  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        270  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        271  |   .2160239   .2376512     0.91   0.367    -.2590341    .6910819
        272  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        274  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        275  |   .2160239   .2376512     0.91   0.367    -.2590341    .6910819
        276  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        278  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        279  |   .2160239   .2376512     0.91   0.367    -.2590341    .6910819
        280  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
        282  |   1.648072   .2062037     7.99   0.000     1.235876    2.060267
        283  |   .3240358   .3064543     1.06   0.294    -.2885576    .9366292
        284  |   2.648072   .9504159     2.79   0.007     .7482173    4.547926
        288  |   .6480716   .2062037     3.14   0.003     .2358762    1.060267
             |
         trt |  -.6480716   .2062037    -3.14   0.003    -1.060267   -.2358762
       _cons |   1.24e-14          .        .       .            .           .
------------------------------------------------------------------------------

. boottest trt, rep($bootstrap_reps) level(95) nogr

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueLocalityID, Rademacher weights:
  trt

                           t(62) =    -3.1429
                        Prob>|t| =     0.0040

95% confidence set for null hypothesis expression: [−1.099, −.2289]

. *randomization inf: permutation test, pval
. ritest trt _b[trt], reps($bootstrap_reps) cluster(uniqueLocalityID) strata(g
> e01) seed(546): reg fd i.distXtrXdateFes trt
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       411
-------------+----------------------------------   F(168, 242)     =      3.61
       Model |  39.1148419       168   .23282644   Prob > F        =    0.0000
    Residual |  15.6053528       242  .064484929   R-squared       =    0.7148
-------------+----------------------------------   Adj R-squared   =    0.5168
       Total |  54.7201946       410  .133463889   Root MSE        =    .25394

------------------------------------------------------------------------------
          fd | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          2  |   .1698041   .2694756     0.63   0.529     -.361013    .7006212
          3  |   .2183196   .2810351     0.78   0.438    -.3352676    .7719067
          4  |  -5.83e-15   .3591237    -0.00   1.000    -.7074074    .7074074
          5  |   .2809152   .2694756     1.04   0.298    -.2499019    .8117323
          6  |   .2183196   .2810351     0.78   0.438    -.3352676    .7719067
          8  |   .2809152   .2694756     1.04   0.298    -.2499019    .8117323
          9  |   .2183196   .2810351     0.78   0.438    -.3352676    .7719067
         14  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
         17  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
         18  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
         20  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
         21  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
         22  |  -5.82e-15   .3591237    -0.00   1.000    -.7074074    .7074074
         23  |   .1698041   .2694756     0.63   0.529     -.361013    .7006212
         24  |   .2183196   .2810351     0.78   0.438    -.3352676    .7719067
         25  |  -5.80e-15   .3591237    -0.00   1.000    -.7074074    .7074074
         26  |   .1698041   .2694756     0.63   0.529     -.361013    .7006212
         27  |   .2183196   .2810351     0.78   0.438    -.3352676    .7719067
         28  |  -5.83e-15   .3591237    -0.00   1.000    -.7074074    .7074074
         29  |   .1698041   .2694756     0.63   0.529     -.361013    .7006212
         30  |   .2183196   .2810351     0.78   0.438    -.3352676    .7719067
         32  |   .2183196   .2771844     0.79   0.432    -.3276826    .7643217
         35  |   .2183196   .2867141     0.76   0.447    -.3464542    .7830933
         36  |   .2183196   .3135701     0.70   0.487    -.3993555    .8359946
         37  |   .2183196   .2810351     0.78   0.438    -.3352676    .7719067
         38  |   .4137397   .2854917     1.45   0.149    -.1486261    .9761055
         39  |   .2183196   .2810351     0.78   0.438    -.3352676    .7719067
         40  |   .4137397   .2854917     1.45   0.149    -.1486261    .9761055
         41  |   .5516529    .295937     1.86   0.064    -.0312883    1.134594
         42  |   .4137397   .2854917     1.45   0.149    -.1486261    .9761055
         48  |    1.21832   .3613429     3.37   0.001     .5065409    1.930098
         50  |    1.21832   .3613429     3.37   0.001     .5065409    1.930098
         51  |   .2183196   .2867141     0.76   0.447    -.3464542    .7830933
         52  |   .2183196    .295937     0.74   0.461    -.3646216    .8012607
         53  |   .2183196   .2867141     0.76   0.447    -.3464542    .7830933
         54  |   .2183196   .3135701     0.70   0.487    -.3993555    .8359946
         55  |   .2183196   .2867141     0.76   0.447    -.3464542    .7830933
         56  |   .1637397   .2854917     0.57   0.567    -.3986261    .7261055
         57  |    1.21832    .295937     4.12   0.000     .6353784    1.801261
         58  |    1.10916   .3116522     3.56   0.000     .4952626    1.723057
         61  |   .7183196   .3135701     2.29   0.023     .1006445    1.335995
         62  |   .1455464   .2944325     0.49   0.622    -.4344312    .7255239
         63  |   .1091598   .2846153     0.38   0.702    -.4514797    .6697993
         64  |  -5.86e-15   .3591237    -0.00   1.000    -.7074074    .7074074
         65  |   .7183196   .3135701     2.29   0.023     .1006445    1.335995
         66  |   .1455464   .2944325     0.49   0.622    -.4344312    .7255239
         67  |   .6091598   .2846153     2.14   0.033     .0485203    1.169799
         68  |  -5.82e-15   .3591237    -0.00   1.000    -.7074074    .7074074
         69  |    1.21832   .3613429     3.37   0.001     .5065409    1.930098
         71  |   .6091598   .2846153     2.14   0.033     .0485203    1.169799
         72  |  -5.83e-15   .3591237    -0.00   1.000    -.7074074    .7074074
         74  |          1   .3591237     2.78   0.006     .2925926    1.707407
         82  |  -5.84e-15   .3591237    -0.00   1.000    -.7074074    .7074074
         89  |   .2183196   .3135701     0.70   0.487    -.3993555    .8359946
         90  |   .1455464   .2944325     0.49   0.622    -.4344312    .7255239
         91  |   .1091598   .2846153     0.38   0.702    -.4514797    .6697993
         92  |  -5.81e-15   .3591237    -0.00   1.000    -.7074074    .7074074
         93  |   .2183196   .3135701     0.70   0.487    -.3993555    .8359946
         94  |   .1455464   .2944325     0.49   0.622    -.4344312    .7255239
         95  |   .1091598   .2846153     0.38   0.702    -.4514797    .6697993
         96  |  -5.80e-15   .3591237    -0.00   1.000    -.7074074    .7074074
         97  |   .2183196   .3135701     0.70   0.487    -.3993555    .8359946
         98  |   .1455464   .2944325     0.49   0.622    -.4344312    .7255239
         99  |   .1091598   .2846153     0.38   0.702    -.4514797    .6697993
        100  |  -5.84e-15   .3591237    -0.00   1.000    -.7074074    .7074074
        101  |    1.21832   .3135701     3.89   0.000     .6006445    1.835995
        102  |          1   .3591237     2.78   0.006     .2925926    1.707407
        103  |    1.21832   .3613429     3.37   0.001     .5065409    1.930098
        104  |          1   .3591237     2.78   0.006     .2925926    1.707407
        105  |    1.21832   .3613429     3.37   0.001     .5065409    1.930098
        107  |   .4788797   .2944325     1.63   0.105    -.1010978    1.058857
        108  |  -5.80e-15   .3591237    -0.00   1.000    -.7074074    .7074074
        109  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
        110  |   .2183196    .295937     0.74   0.461    -.3646216    .8012607
        111  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
        112  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
        113  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
        114  |   .8849862    .295937     2.99   0.003     .3020451    1.467927
        115  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
        116  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
        117  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
        118  |   .8849862    .295937     2.99   0.003     .3020451    1.467927
        119  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
        120  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
        137  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
        138  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
        139  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
        140  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
        141  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
        142  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
        143  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
        144  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
        145  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
        146  |   .2183196   .3135701     0.70   0.487    -.3993555    .8359946
        147  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
        148  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
        149  |    1.21832   .3613429     3.37   0.001     .5065409    1.930098
        150  |   .7183196   .3135701     2.29   0.023     .1006445    1.335995
        151  |    1.21832   .3613429     3.37   0.001     .5065409    1.930098
        152  |    1.21832   .3613429     3.37   0.001     .5065409    1.930098
        154  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
        157  |  -5.85e-15   .3591237    -0.00   1.000    -.7074074    .7074074
        158  |   .1455464   .2944325     0.49   0.622    -.4344312    .7255239
        159  |  -5.84e-15   .3591237    -0.00   1.000    -.7074074    .7074074
        160  |   .4788797   .2944325     1.63   0.105    -.1010978    1.058857
        161  |  -5.83e-15   .3591237    -0.00   1.000    -.7074074    .7074074
        162  |   .1455464   .2944325     0.49   0.622    -.4344312    .7255239
        171  |   .1091598   .3116522     0.35   0.726    -.5047374     .723057
        172  |   .1455464   .2944325     0.49   0.622    -.4344312    .7255239
        173  |  -5.84e-15   .3591237    -0.00   1.000    -.7074074    .7074074
        174  |   .1455464   .2944325     0.49   0.622    -.4344312    .7255239
        175  |   .1091598   .3116522     0.35   0.726    -.5047374     .723057
        176  |   .1455464   .2944325     0.49   0.622    -.4344312    .7255239
        177  |          1   .3591237     2.78   0.006     .2925926    1.707407
        178  |   .6091598   .3116522     1.95   0.052    -.0047374    1.223057
        179  |          1   .3591237     2.78   0.006     .2925926    1.707407
        182  |   .5516529    .295937     1.86   0.064    -.0312883    1.134594
        184  |   .2183196    .295937     0.74   0.461    -.3646216    .8012607
        186  |   .5516529    .295937     1.86   0.064    -.0312883    1.134594
        196  |   .2183196    .295937     0.74   0.461    -.3646216    .8012607
        198  |   .5516529    .295937     1.86   0.064    -.0312883    1.134594
        200  |   .2183196    .295937     0.74   0.461    -.3646216    .8012607
        202  |    1.21832   .3613429     3.37   0.001     .5065409    1.930098
        205  |   .1637397   .2854917     0.57   0.567    -.3986261    .7261055
        206  |   .9137397   .2854917     3.20   0.002     .3513739    1.476105
        207  |   .9137397   .2854917     3.20   0.002     .3513739    1.476105
        212  |   .1637397   .2854917     0.57   0.567    -.3986261    .7261055
        213  |   .1637397   .2854917     0.57   0.567    -.3986261    .7261055
        214  |   .1637397   .2854917     0.57   0.567    -.3986261    .7261055
        215  |    1.21832   .3613429     3.37   0.001     .5065409    1.930098
        217  |   .1455464   .2944325     0.49   0.622    -.4344312    .7255239
        218  |  -5.82e-15   .3591237    -0.00   1.000    -.7074074    .7074074
        219  |    .812213   .2944325     2.76   0.006     .2322355    1.392191
        220  |          1   .3591237     2.78   0.006     .2925926    1.707407
        221  |    .812213   .2944325     2.76   0.006     .2322355    1.392191
        222  |          1   .3591237     2.78   0.006     .2925926    1.707407
        227  |  -5.89e-15   .3591237    -0.00   1.000    -.7074074    .7074074
        231  |   .1455464   .2944325     0.49   0.622    -.4344312    .7255239
        232  |  -5.80e-15   .3591237    -0.00   1.000    -.7074074    .7074074
        233  |   .2183196   .3135701     0.70   0.487    -.3993555    .8359946
        234  |  -5.86e-15   .3591237    -0.00   1.000    -.7074074    .7074074
        235  |   .4788797   .2944325     1.63   0.105    -.1010978    1.058857
        236  |  -5.81e-15   .3591237    -0.00   1.000    -.7074074    .7074074
        237  |          1   .3591237     2.78   0.006     .2925926    1.707407
        239  |          1   .3591237     2.78   0.006     .2925926    1.707407
        242  |   .2183196   .3135701     0.70   0.487    -.3993555    .8359946
        243  |   .1091598   .3116522     0.35   0.726    -.5047374     .723057
        244  |   .2183196   .3135701     0.70   0.487    -.3993555    .8359946
        246  |   .2183196   .3135701     0.70   0.487    -.3993555    .8359946
        247  |   .4061065   .2935261     1.38   0.168    -.1720856    .9842986
        248  |   .2183196   .3135701     0.70   0.487    -.3993555    .8359946
        250  |   .2183196   .3135701     0.70   0.487    -.3993555    .8359946
        251  |   .1091598   .3116522     0.35   0.726    -.5047374     .723057
        252  |   .2183196   .3135701     0.70   0.487    -.3993555    .8359946
        270  |   .2183196   .3135701     0.70   0.487    -.3993555    .8359946
        271  |   .0727732   .2935261     0.25   0.804    -.5054189    .6509653
        272  |   .2183196   .3135701     0.70   0.487    -.3993555    .8359946
        274  |   .2183196   .3135701     0.70   0.487    -.3993555    .8359946
        275  |   .0727732   .2935261     0.25   0.804    -.5054189    .6509653
        276  |   .2183196   .3135701     0.70   0.487    -.3993555    .8359946
        278  |   .2183196   .3135701     0.70   0.487    -.3993555    .8359946
        279  |   .0727732   .2935261     0.25   0.804    -.5054189    .6509653
        280  |   .2183196   .3135701     0.70   0.487    -.3993555    .8359946
        282  |    1.21832   .3135701     3.89   0.000     .6006445    1.835995
        283  |   .1091598   .3116522     0.35   0.726    -.5047374     .723057
        284  |    1.21832   .3135701     3.89   0.000     .6006445    1.835995
        288  |   .2183196   .3613429     0.60   0.546    -.4934591    .9300982
             |
         trt |  -.2183196    .039985    -5.46   0.000    -.2970826   -.1395565
       _cons |   5.86e-15   .2539388     0.00   1.000    -.5002125    .5002125
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000

      Command: regress fd i.distXtrXdateFes trt
        _pm_1: _b[trt]
  res. var(s):  trt
   Resampling:  Permuting trt
Clust. var(s):  uniqueLocalityID
     Clusters:  78
Strata var(s):  ge01
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |  -.2183196       0    1000  0.0000  0.0000         0   .0036821
------------------------------------------------------------------------------
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. ritest trt _b[trt], reps($bootstrap_reps) cluster(uniqueLocalityID) strata(g
> e01) seed(546): reg fdamt i.distXtrXdateFes trt
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       411
-------------+----------------------------------   F(168, 242)     =      3.18
       Model |  411.429126       168  2.44898289   Prob > F        =    0.0000
    Residual |  186.254572       242  .769646993   R-squared       =    0.6884
-------------+----------------------------------   Adj R-squared   =    0.4720
       Total |  597.683698       410  1.45776512   Root MSE        =     .8773

------------------------------------------------------------------------------
       fdamt | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          2  |   .5040557   .9309709     0.54   0.589    -1.329785    2.337896
          3  |   .6480716    .970906     0.67   0.505    -1.264434    2.560577
          4  |  -1.23e-14   1.240683    -0.00   1.000    -2.443916    2.443916
          5  |   .9485002   .9309709     1.02   0.309    -.8853405    2.782341
          6  |   .6480716    .970906     0.67   0.505    -1.264434    2.560577
          8  |   .6151668   .9309709     0.66   0.509    -1.218674    2.449007
          9  |   .6480716    .970906     0.67   0.505    -1.264434    2.560577
         14  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
         17  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
         18  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
         20  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
         21  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
         22  |  -1.23e-14   1.240683    -0.00   1.000    -2.443916    2.443916
         23  |   .5040557   .9309709     0.54   0.589    -1.329785    2.337896
         24  |   .6480716    .970906     0.67   0.505    -1.264434    2.560577
         25  |  -1.23e-14   1.240683    -0.00   1.000    -2.443916    2.443916
         26  |   .5040557   .9309709     0.54   0.589    -1.329785    2.337896
         27  |   .6480716    .970906     0.67   0.505    -1.264434    2.560577
         28  |  -1.24e-14   1.240683    -0.00   1.000    -2.443916    2.443916
         29  |   .5040557   .9309709     0.54   0.589    -1.329785    2.337896
         30  |   .6480716    .970906     0.67   0.505    -1.264434    2.560577
         32  |   .6480716   .9576031     0.68   0.499    -1.238229    2.534373
         35  |   .6480716   .9905256     0.65   0.514    -1.303081    2.599224
         36  |   .6480716   1.083306     0.60   0.550    -1.485842    2.781985
         37  |   .6480716    .970906     0.67   0.505    -1.264434    2.560577
         38  |   1.736054   .9863024     1.76   0.080    -.2067797    3.678887
         39  |   .6480716    .970906     0.67   0.505    -1.264434    2.560577
         40  |   1.486054   .9863024     1.51   0.133    -.4567797    3.428887
         41  |    .981405   1.022388     0.96   0.338    -1.032511    2.995321
         42  |   .7360537   .9863024     0.75   0.456     -1.20678    2.678887
         48  |   5.648072   1.248349     4.52   0.000     3.189054    8.107089
         50  |   10.64807   1.248349     8.53   0.000     8.189054    13.10709
         51  |   .6480716   .9905256     0.65   0.514    -1.303081    2.599224
         52  |   .6480716   1.022388     0.63   0.527    -1.365845    2.661988
         53  |   .6480716   .9905256     0.65   0.514    -1.303081    2.599224
         54  |   .6480716   1.083306     0.60   0.550    -1.485842    2.781985
         55  |   .6480716   .9905256     0.65   0.514    -1.303081    2.599224
         56  |   .4860537   .9863024     0.49   0.623     -1.45678    2.428887
         57  |   1.648072   1.022388     1.61   0.108    -.3658446    3.661988
         58  |   2.324036   1.076681     2.16   0.032     .2031742    4.444897
         61  |   3.148072   1.083306     2.91   0.004     1.014158    5.281985
         62  |   .4320478   1.017191     0.42   0.671     -1.57163    2.435725
         63  |   .3240358   .9832748     0.33   0.742    -1.612834    2.260905
         64  |  -1.23e-14   1.240683    -0.00   1.000    -2.443916    2.443916
         65  |   2.648072   1.083306     2.44   0.015     .5141584    4.781985
         66  |   .4320478   1.017191     0.42   0.671     -1.57163    2.435725
         67  |   2.324036   .9832748     2.36   0.019     .3871663    4.260905
         68  |  -1.22e-14   1.240683    -0.00   1.000    -2.443916    2.443916
         69  |   1.648072   1.248349     1.32   0.188    -.8109459    4.107089
         71  |   .8240358   .9832748     0.84   0.403    -1.112834    2.760905
         72  |  -1.23e-14   1.240683    -0.00   1.000    -2.443916    2.443916
         74  |          5   1.240683     4.03   0.000     2.556084    7.443916
         82  |  -1.23e-14   1.240683    -0.00   1.000    -2.443916    2.443916
         89  |   .6480716   1.083306     0.60   0.550    -1.485842    2.781985
         90  |   .4320478   1.017191     0.42   0.671     -1.57163    2.435725
         91  |   .3240358   .9832748     0.33   0.742    -1.612834    2.260905
         92  |  -1.23e-14   1.240683    -0.00   1.000    -2.443916    2.443916
         93  |   .6480716   1.083306     0.60   0.550    -1.485842    2.781985
         94  |   .4320478   1.017191     0.42   0.671     -1.57163    2.435725
         95  |   .3240358   .9832748     0.33   0.742    -1.612834    2.260905
         96  |  -1.22e-14   1.240683    -0.00   1.000    -2.443916    2.443916
         97  |   .6480716   1.083306     0.60   0.550    -1.485842    2.781985
         98  |   .4320478   1.017191     0.42   0.671     -1.57163    2.435725
         99  |   .3240358   .9832748     0.33   0.742    -1.612834    2.260905
        100  |  -1.24e-14   1.240683    -0.00   1.000    -2.443916    2.443916
        101  |   1.648072   1.083306     1.52   0.129    -.4858416    3.781985
        102  |          1   1.240683     0.81   0.421    -1.443916    3.443916
        103  |   1.648072   1.248349     1.32   0.188    -.8109459    4.107089
        104  |          1   1.240683     0.81   0.421    -1.443916    3.443916
        105  |   3.648072   1.248349     2.92   0.004     1.189054    6.107089
        107  |   1.432048   1.017191     1.41   0.160    -.5716299    3.435725
        108  |  -1.22e-14   1.240683    -0.00   1.000    -2.443916    2.443916
        109  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
        110  |   .6480716   1.022388     0.63   0.527    -1.365845    2.661988
        111  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
        112  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
        113  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
        114  |   3.314738   1.022388     3.24   0.001     1.300822    5.328655
        115  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
        116  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
        117  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
        118  |   1.314738   1.022388     1.29   0.200     -.699178    3.328655
        119  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
        120  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
        137  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
        138  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
        139  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
        140  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
        141  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
        142  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
        143  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
        144  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
        145  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
        146  |   .6480716   1.083306     0.60   0.550    -1.485842    2.781985
        147  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
        148  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
        149  |   1.648072   1.248349     1.32   0.188    -.8109459    4.107089
        150  |   1.148072   1.083306     1.06   0.290    -.9858416    3.281985
        151  |   1.648072   1.248349     1.32   0.188    -.8109459    4.107089
        152  |   1.648072   1.248349     1.32   0.188    -.8109459    4.107089
        154  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
        157  |  -1.23e-14   1.240683    -0.00   1.000    -2.443916    2.443916
        158  |   .4320478   1.017191     0.42   0.671     -1.57163    2.435725
        159  |  -1.23e-14   1.240683    -0.00   1.000    -2.443916    2.443916
        160  |   1.765381   1.017191     1.74   0.084    -.2382966    3.769059
        161  |  -1.22e-14   1.240683    -0.00   1.000    -2.443916    2.443916
        162  |   .4320478   1.017191     0.42   0.671     -1.57163    2.435725
        171  |   .3240358   1.076681     0.30   0.764    -1.796826    2.444897
        172  |   .4320478   1.017191     0.42   0.671     -1.57163    2.435725
        173  |  -1.24e-14   1.240683    -0.00   1.000    -2.443916    2.443916
        174  |   .4320478   1.017191     0.42   0.671     -1.57163    2.435725
        175  |   .3240358   1.076681     0.30   0.764    -1.796826    2.444897
        176  |   .4320478   1.017191     0.42   0.671     -1.57163    2.435725
        177  |          1   1.240683     0.81   0.421    -1.443916    3.443916
        178  |   .8240358   1.076681     0.77   0.445    -1.296826    2.944897
        179  |          5   1.240683     4.03   0.000     2.556084    7.443916
        182  |   2.314738   1.022388     2.26   0.024      .300822    4.328655
        184  |   .6480716   1.022388     0.63   0.527    -1.365845    2.661988
        186  |    .981405   1.022388     0.96   0.338    -1.032511    2.995321
        196  |   .6480716   1.022388     0.63   0.527    -1.365845    2.661988
        198  |   2.314738   1.022388     2.26   0.024      .300822    4.328655
        200  |   .6480716   1.022388     0.63   0.527    -1.365845    2.661988
        202  |   3.648072   1.248349     2.92   0.004     1.189054    6.107089
        205  |   .4860537   .9863024     0.49   0.623     -1.45678    2.428887
        206  |   3.486054   .9863024     3.53   0.000      1.54322    5.428887
        207  |   1.236054   .9863024     1.25   0.211    -.7067797    3.178887
        212  |   .4860537   .9863024     0.49   0.623     -1.45678    2.428887
        213  |   .4860537   .9863024     0.49   0.623     -1.45678    2.428887
        214  |   .4860537   .9863024     0.49   0.623     -1.45678    2.428887
        215  |   5.648072   1.248349     4.52   0.000     3.189054    8.107089
        217  |   .4320478   1.017191     0.42   0.671     -1.57163    2.435725
        218  |  -1.22e-14   1.240683    -0.00   1.000    -2.443916    2.443916
        219  |   3.098714   1.017191     3.05   0.003     1.095037    5.102392
        220  |          4   1.240683     3.22   0.001     1.556084    6.443916
        221  |   1.098714   1.017191     1.08   0.281    -.9049632    3.102392
        222  |          1   1.240683     0.81   0.421    -1.443916    3.443916
        227  |  -1.24e-14   1.240683    -0.00   1.000    -2.443916    2.443916
        231  |   .4320478   1.017191     0.42   0.671     -1.57163    2.435725
        232  |  -1.22e-14   1.240683    -0.00   1.000    -2.443916    2.443916
        233  |   .6480716   1.083306     0.60   0.550    -1.485842    2.781985
        234  |  -1.23e-14   1.240683    -0.00   1.000    -2.443916    2.443916
        235  |   2.098714   1.017191     2.06   0.040     .0950368    4.102392
        236  |  -1.22e-14   1.240683    -0.00   1.000    -2.443916    2.443916
        237  |          3   1.240683     2.42   0.016     .5560841    5.443916
        239  |          4   1.240683     3.22   0.001     1.556084    6.443916
        242  |   .6480716   1.083306     0.60   0.550    -1.485842    2.781985
        243  |   .3240358   1.076681     0.30   0.764    -1.796826    2.444897
        244  |   .6480716   1.083306     0.60   0.550    -1.485842    2.781985
        246  |   .6480716   1.083306     0.60   0.550    -1.485842    2.781985
        247  |   1.549357   1.014059     1.53   0.128    -.4481521    3.546867
        248  |   .6480716   1.083306     0.60   0.550    -1.485842    2.781985
        250  |   .6480716   1.083306     0.60   0.550    -1.485842    2.781985
        251  |   .3240358   1.076681     0.30   0.764    -1.796826    2.444897
        252  |   .6480716   1.083306     0.60   0.550    -1.485842    2.781985
        270  |   .6480716   1.083306     0.60   0.550    -1.485842    2.781985
        271  |   .2160239   1.014059     0.21   0.831    -1.781485    2.213533
        272  |   .6480716   1.083306     0.60   0.550    -1.485842    2.781985
        274  |   .6480716   1.083306     0.60   0.550    -1.485842    2.781985
        275  |   .2160239   1.014059     0.21   0.831    -1.781485    2.213533
        276  |   .6480716   1.083306     0.60   0.550    -1.485842    2.781985
        278  |   .6480716   1.083306     0.60   0.550    -1.485842    2.781985
        279  |   .2160239   1.014059     0.21   0.831    -1.781485    2.213533
        280  |   .6480716   1.083306     0.60   0.550    -1.485842    2.781985
        282  |   1.648072   1.083306     1.52   0.129    -.4858416    3.781985
        283  |   .3240358   1.076681     0.30   0.764    -1.796826    2.444897
        284  |   2.648072   1.083306     2.44   0.015     .5141584    4.781985
        288  |   .6480716   1.248349     0.52   0.604    -1.810946    3.107089
             |
         trt |  -.6480716   .1381382    -4.69   0.000    -.9201783   -.3759649
       _cons |   1.24e-14   .8772953     0.00   1.000     -1.72811     1.72811
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000

      Command: regress fdamt i.distXtrXdateFes trt
        _pm_1: _b[trt]
  res. var(s):  trt
   Resampling:  Permuting trt
Clust. var(s):  uniqueLocalityID
     Clusters:  78
Strata var(s):  ge01
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |  -.6480716       0    1000  0.0000  0.0000         0   .0036821
------------------------------------------------------------------------------
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. *mht: implement Romano-Wolf (2005) procedure, pval
. rwolf fd fdamt ihs_fdamt, indepvar(trt trt2 trt3 trt4) reps($bootstrap_reps)
>  seed(124) controls(i.distXtrXdateFes) //family (misconduct: 0/1, amount)
Bootstrap replications (1000). This may take some time.
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..................................................     50
..................................................     100
..................................................     150
..................................................     200
..................................................     250
..................................................     300
..................................................     350
..................................................     400
..................................................     450
..................................................     500
..................................................     550
..................................................     600
..................................................     650
..................................................     700
..................................................     750
..................................................     800
..................................................     850
..................................................     900
..................................................     950
..................................................     1000




Romano-Wolf step-down adjusted p-values


Independent variable:  trt
Outcome variables:   fd fdamt ihs_fdamt
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
                 fd |     0.0003             0.0240              0.0440
              fdamt |     0.0019             0.0559              0.0559
          ihs_fdamt |     0.0008             0.0410              0.0490
------------------------------------------------------------------------------


Independent variable:  trt2
Outcome variables:   fd fdamt ihs_fdamt
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
                 fd |     0.2651             0.3097              0.3696
              fdamt |     0.2445             0.2697              0.3696
          ihs_fdamt |     0.2026             0.2398              0.3287
------------------------------------------------------------------------------


Independent variable:  trt3
Outcome variables:   fd fdamt ihs_fdamt
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
                 fd |     0.1500             0.2288              0.3377
              fdamt |     0.2479             0.3956              0.3956
          ihs_fdamt |     0.1922             0.3137              0.3506
------------------------------------------------------------------------------


Independent variable:  trt4
Outcome variables:   fd fdamt ihs_fdamt
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
                 fd |          .             0.0010              0.0010
              fdamt |          .             0.0010              0.0010
          ihs_fdamt |          .             0.0010              0.0010
------------------------------------------------------------------------------



. *attrition bounds-lee
. leebounds fd trt, level(95) cieffect tight()

Lee (2009) treatment effect bounds

Number of obs.                     =   771
Number of selected obs.            =   411
Trimming porportion                =   0.0145
Effect 95% conf. interval          : [-0.3332  -0.0608]

------------------------------------------------------------------------------
          fd | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt          |
       lower |  -.1702524   .0865855    -1.97   0.049    -.3399568   -.0005479
       upper |  -.1555524   .0503692    -3.09   0.002    -.2542742   -.0568307
------------------------------------------------------------------------------

. leebounds fdamt trt, level(95) cieffect tight()

Lee (2009) treatment effect bounds

Number of obs.                     =   771
Number of selected obs.            =   411
Trimming porportion                =   0.0145
Effect 95% conf. interval          : [-1.3434  -0.1667]

------------------------------------------------------------------------------
       fdamt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt          |
       lower |  -.5693988    .415268    -1.37   0.170    -1.383309    .2445115
       upper |  -.4797415   .1679655    -2.86   0.004    -.8089479   -.1505351
------------------------------------------------------------------------------

. *attrition bounds-Behajel et al: denote "all obs selected" or Not applicable
>  here (no phone calls or repeat visits allowed)
. *restore
. 
. *SEPARATE
. keep if _merge==1
(0 observations deleted)

. *wild cluster bootstrap, pval
. reg fd i.distXtrXdateFes trt2 trt3 trt4, r cluster(uniqueLocalityID) level(9
> 5)

Linear regression                               Number of obs     =        405
                                                F(35, 61)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.7168
                                                Root MSE          =     .25699

                      (Std. err. adjusted for 62 clusters in uniqueLocalityID)
------------------------------------------------------------------------------
             |               Robust
          fd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          2  |   .1718625   .0700911     2.45   0.017     .0317066    .3120183
          3  |   .2207983   .0669208     3.30   0.002     .0869818    .3546148
          4  |  -2.35e-15   1.13e-07    -0.00   1.000    -2.25e-07    2.25e-07
          5  |   .2829736   .1472831     1.92   0.059    -.0115371    .5774843
          6  |   .2207983   .0669208     3.30   0.002     .0869818    .3546148
          8  |   .2829736   .1472831     1.92   0.059    -.0115371    .5774843
          9  |   .2207983   .0669208     3.30   0.002     .0869818    .3546148
         14  |   .2392159    .075149     3.18   0.002      .088946    .3894857
         17  |   .2392159    .075149     3.18   0.002      .088946    .3894857
         18  |   .2326852   .0708853     3.28   0.002     .0909414    .3744291
         20  |   .2392159    .075149     3.18   0.002      .088946    .3894857
         21  |   .2326852   .0708853     3.28   0.002     .0909414    .3744291
         22  |  -2.36e-15   1.13e-07    -0.00   1.000    -2.25e-07    2.25e-07
         23  |   .1718625   .0700911     2.45   0.017     .0317066    .3120183
         24  |   .2207983   .0669208     3.30   0.002     .0869818    .3546148
         25  |  -2.34e-15   1.13e-07    -0.00   1.000    -2.25e-07    2.25e-07
         26  |   .1718625   .0700911     2.45   0.017     .0317066    .3120183
         27  |   .2207983   .0669208     3.30   0.002     .0869818    .3546148
         28  |  -2.35e-15          .        .       .            .           .
         29  |   .1718625   .0700911     2.45   0.017     .0317066    .3120183
         30  |   .2207983   .0669208     3.30   0.002     .0869818    .3546148
         32  |   .2280259   .0689661     3.31   0.002     .0901196    .3659322
         35  |   .2240636   .0722932     3.10   0.003     .0795045    .3686227
         36  |    .205646   .0700952     2.93   0.005      .065482      .34581
         37  |   .2231757    .066552     3.35   0.001     .0900966    .3562547
         38  |   .4142596    .252841     1.64   0.106    -.0913272    .9198464
         39  |   .2231757    .066552     3.35   0.001     .0900966    .3562547
         40  |   .4142596    .252841     1.64   0.106    -.0913272    .9198464
         41  |   .5681954   .3638133     1.56   0.124    -.1592942    1.295685
         42  |   .4142596    .252841     1.64   0.106    -.0913272    .9198464
         48  |   1.239216    .075149    16.49   0.000     1.088946    1.389486
         50  |   1.239216    .075149    16.49   0.000     1.088946    1.389486
         51  |   .2207983   .0667934     3.31   0.002     .0872366      .35436
         52  |   .2190128   .0727999     3.01   0.004     .0734404    .3645853
         53  |   .2207983   .0667934     3.31   0.002     .0872366      .35436
         54  |   .2392159    .075149     3.18   0.002      .088946    .3894857
         55  |   .2207983   .0667934     3.31   0.002     .0872366      .35436
         56  |   .1642596   .0624883     2.63   0.011     .0393065    .2892127
         57  |   1.234862   .0663752    18.60   0.000     1.102137    1.367587
         58  |   1.119608   .1046253    10.70   0.000     .9103967    1.328819
         61  |   .7089113   .4452485     1.59   0.117     -.181418    1.599241
         62  |   .1392742     .09539     1.46   0.149    -.0514698    .3300182
         63  |   .1163426   .0828295     1.40   0.165    -.0492851    .2819703
         64  |  -2.35e-15   1.13e-07    -0.00   1.000    -2.25e-07    2.25e-07
         65  |   .7089113   .4452485     1.59   0.117     -.181418    1.599241
         66  |   .1392742     .09539     1.46   0.149    -.0514698    .3300182
         67  |   .6163426   .2587075     2.38   0.020      .099025     1.13366
         68  |  -2.35e-15   1.13e-07    -0.00   1.000    -2.25e-07    2.25e-07
         69  |   1.178607   .0706372    16.69   0.000     1.037359    1.319855
         71  |   .6163426   .2587075     2.38   0.020      .099025     1.13366
         72  |  -2.34e-15   2.15e-08    -0.00   1.000    -4.31e-08    4.31e-08
         74  |          1   1.11e-07  9.0e+06   0.000     .9999998           1
         82  |  -2.35e-15   2.15e-08    -0.00   1.000    -4.31e-08    4.31e-08
         89  |   .2089113   .0740643     2.82   0.006     .0608106     .357012
         90  |   .1392742     .09539     1.46   0.149    -.0514698    .3300182
         91  |   .1163426   .0828295     1.40   0.165    -.0492851    .2819703
         92  |  -2.35e-15   1.13e-07    -0.00   1.000    -2.25e-07    2.25e-07
         93  |   .2089113   .0740643     2.82   0.006     .0608106     .357012
         94  |   .1392742     .09539     1.46   0.149    -.0514698    .3300182
         95  |   .1163426   .0828295     1.40   0.165    -.0492851    .2819703
         96  |  -2.33e-15   1.13e-07    -0.00   1.000    -2.25e-07    2.25e-07
         97  |   .2089113   .0740643     2.82   0.006     .0608106     .357012
         98  |   .1392742     .09539     1.46   0.149    -.0514698    .3300182
         99  |   .1163426   .0828295     1.40   0.165    -.0492851    .2819703
        100  |  -2.33e-15   1.13e-07    -0.00   1.000    -2.25e-07    2.25e-07
        101  |   1.208911   .0740643    16.32   0.000     1.060811    1.357012
        102  |          1   1.13e-07  8.9e+06   0.000     .9999998           1
        103  |   1.232685   .0708853    17.39   0.000     1.090941    1.374429
        104  |          1   1.13e-07  8.9e+06   0.000     .9999998           1
        105  |   1.178607   .0706372    16.69   0.000     1.037359    1.319855
        107  |   .4884568   .2875333     1.70   0.094    -.0865016    1.063415
        108  |  -2.31e-15   1.13e-07    -0.00   1.000    -2.25e-07    2.25e-07
        109  |   .2326852   .0708853     3.28   0.002     .0909414    .3744291
        110  |    .216836   .0676718     3.20   0.002     .0815178    .3521541
        111  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        112  |   .2392159    .075149     3.18   0.002      .088946    .3894857
        113  |   .2326852   .0708853     3.28   0.002     .0909414    .3744291
        114  |   .8835026   .3559001     2.48   0.016     .1718364    1.595169
        115  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        116  |   .2392159    .075149     3.18   0.002      .088946    .3894857
        117  |   .2326852   .0708853     3.28   0.002     .0909414    .3744291
        118  |   .8835026   .3559001     2.48   0.016     .1718364    1.595169
        119  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        120  |   .2392159    .075149     3.18   0.002      .088946    .3894857
        137  |   .2326852   .0708853     3.28   0.002     .0909414    .3744291
        138  |   .2392159    .075149     3.18   0.002      .088946    .3894857
        139  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        140  |   .2392159    .075149     3.18   0.002      .088946    .3894857
        141  |   .2326852   .0708853     3.28   0.002     .0909414    .3744291
        142  |   .2392159    .075149     3.18   0.002      .088946    .3894857
        143  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        144  |   .2392159    .075149     3.18   0.002      .088946    .3894857
        145  |   .2326852   .0708853     3.28   0.002     .0909414    .3744291
        146  |   .2359505    .066597     3.54   0.001     .1027815    .3691196
        147  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        148  |   .2392159    .075149     3.18   0.002      .088946    .3894857
        149  |   1.232685   .0708853    17.39   0.000     1.090941    1.374429
        150  |   .7359505   .4661626     1.58   0.120    -.1961991      1.6681
        151  |   1.178607   .0706372    16.69   0.000     1.037359    1.319855
        152  |   1.239216    .075149    16.49   0.000     1.088946    1.389486
        154  |   .2392159    .075149     3.18   0.002      .088946    .3894857
        157  |  -2.34e-15   6.01e-08    -0.00   1.000    -1.20e-07    1.20e-07
        158  |   .1370973   .0861825     1.59   0.117    -.0352352    .3094299
        159  |  -2.32e-15   9.56e-08    -0.00   1.000    -1.91e-07    1.91e-07
        160  |   .4704307   .2904013     1.62   0.110    -.1102625    1.051124
        161  |  -2.37e-15   6.01e-08    -0.00   1.000    -1.20e-07    1.20e-07
        162  |   .1370973   .0861825     1.59   0.117    -.0352352    .3094299
        171  |   .1196079   .1181692     1.01   0.315    -.1166861    .3559019
        172  |   .1370973   .0861825     1.59   0.117    -.0352352    .3094299
        173  |  -2.35e-15   6.01e-08    -0.00   1.000    -1.20e-07    1.20e-07
        174  |   .1370973   .0861825     1.59   0.117    -.0352352    .3094299
        175  |   .1196079   .1181692     1.01   0.315    -.1166861    .3559019
        176  |   .1370973   .0861825     1.59   0.117    -.0352352    .3094299
        177  |          1   1.13e-07  8.9e+06   0.000     .9999998           1
        178  |   .5893034    .384306     1.53   0.130    -.1791639    1.357771
        179  |          1   6.01e-08  1.7e+07   0.000     .9999999           1
        182  |   .5321431    .352904     1.51   0.137     -.173532    1.237818
        184  |   .1988098    .071793     2.77   0.007     .0552509    .3423687
        186  |   .5321431    .352904     1.51   0.137     -.173532    1.237818
        196  |   .1988098    .071793     2.77   0.007     .0552509    .3423687
        198  |   .5321431   .3892094     1.37   0.177    -.2461292    1.310416
        200  |   .1988098    .071793     2.77   0.007     .0552509    .3423687
        202  |   1.178607   .0706372    16.69   0.000     1.037359    1.319855
        205  |    .162627   .0864825     1.88   0.065    -.0103054    .3355593
        206  |    .912627   .2602072     3.51   0.001     .3923105    1.432943
        207  |    .912627   .2602072     3.51   0.001     .3923105    1.432943
        212  |    .162627   .0864825     1.88   0.065    -.0103054    .3355593
        213  |    .162627   .0864825     1.88   0.065    -.0103054    .3355593
        214  |    .162627   .0864825     1.88   0.065    -.0103054    .3355593
        215  |   1.232685   .0708853    17.39   0.000     1.090941    1.374429
        217  |   .1190712   .0824388     1.44   0.154    -.0457754    .2839178
        218  |  -2.36e-15   1.13e-07    -0.00   1.000    -2.25e-07    2.25e-07
        219  |   .7857378   .4308507     1.82   0.073    -.0758013    1.647277
        220  |          1   1.13e-07  8.9e+06   0.000     .9999998           1
        221  |   .7857378   .3282385     2.39   0.020     .1293843    1.442091
        222  |          1   1.13e-07  8.9e+06   0.000     .9999998           1
        227  |  -2.34e-15   1.13e-07    -0.00   1.000    -2.25e-07    2.25e-07
        231  |   .1190712   .0824388     1.44   0.154    -.0457754    .2839178
        232  |  -2.34e-15   1.13e-07    -0.00   1.000    -2.25e-07    2.25e-07
        233  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        234  |  -2.35e-15   1.13e-07    -0.00   1.000    -2.25e-07    2.25e-07
        235  |   .4524045   .2965303     1.53   0.132    -.1405445    1.045354
        236  |  -2.36e-15   1.13e-07    -0.00   1.000    -2.25e-07    2.25e-07
        237  |          1   1.12e-07  8.9e+06   0.000     .9999998           1
        239  |          1   1.13e-07  8.9e+06   0.000     .9999998           1
        242  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        243  |   .1196079   .1166667     1.03   0.309    -.1136816    .3528974
        244  |   .2359505   .0664197     3.55   0.001     .1031362    .3687649
        246  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        247  |    .413072    .330431     1.25   0.216    -.2476657     1.07381
        248  |   .2359505   .0664197     3.55   0.001     .1031362    .3687649
        250  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        251  |   .1196079   .1126674     1.06   0.293    -.1056844    .3449002
        252  |   .2359505   .0664197     3.55   0.001     .1031362    .3687649
        270  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        271  |   .0797386   .0870915     0.92   0.363    -.0944116    .2538888
        272  |   .2359505   .0664197     3.55   0.001     .1031362    .3687649
        274  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        275  |   .0797386   .0870915     0.92   0.363    -.0944116    .2538888
        276  |   .2359505   .0664197     3.55   0.001     .1031362    .3687649
        278  |   .1786068   .0706372     2.53   0.014     .0373589    .3198546
        279  |   .0797386   .0870915     0.92   0.363    -.0944116    .2538888
        280  |   .2359505   .0664197     3.55   0.001     .1031362    .3687649
        282  |   1.178607   .0706372    16.69   0.000     1.037359    1.319855
        283  |   .1196079   .1126674     1.06   0.293    -.1056844    .3449002
        284  |   1.235951   .0664197    18.61   0.000     1.103136    1.368765
        288  |   .2392159    .075149     3.18   0.002      .088946    .3894857
             |
        trt2 |  -.2326852   .0708853    -3.28   0.002    -.3744291   -.0909414
        trt3 |  -.2392159    .075149    -3.18   0.002    -.3894857    -.088946
        trt4 |  -.1786068   .0706372    -2.53   0.014    -.3198546   -.0373589
       _cons |   2.33e-15          .        .       .            .           .
------------------------------------------------------------------------------

. boottest trt2, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueLocalityID, Rademacher weights:
  trt2

                           t(61) =    -3.2826
                        Prob>|t| =     0.0030

95% confidence set for null hypothesis expression: [−.379, −.08436]

. boottest trt3, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueLocalityID, Rademacher weights:
  trt3

                           t(61) =    -3.1832
                        Prob>|t| =     0.0000

95% confidence set for null hypothesis expression: [−.4004, −.08676]

. boottest trt4, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueLocalityID, Rademacher weights:
  trt4

                           t(61) =    -2.5285
                        Prob>|t| =     0.0090

95% confidence set for null hypothesis expression: [−.3218, −.03813]

. reg fdamt i.distXtrXdateFes trt2 trt3 trt4, r cluster(uniqueLocalityID) leve
> l(95)

Linear regression                               Number of obs     =        405
                                                F(35, 61)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6902
                                                Root MSE          =     .88876

                      (Std. err. adjusted for 62 clusters in uniqueLocalityID)
------------------------------------------------------------------------------
             |               Robust
       fdamt | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          2  |    .504751   .2206156     2.29   0.026     .0636027    .9458992
          3  |   .6646843   .2061521     3.22   0.002     .2524577    1.076911
          4  |   8.60e-16   4.35e-07     0.00   1.000    -8.70e-07    8.70e-07
          5  |   .9491954   .5577923     1.70   0.094    -.1661793     2.06457
          6  |   .6646843   .2061521     3.22   0.002     .2524577    1.076911
          8  |   .6158621   .2377296     2.59   0.012     .1404924    1.091232
          9  |   .6646843   .2061521     3.22   0.002     .2524577    1.076911
         14  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
         17  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
         18  |   .7203142   .1966734     3.66   0.001     .3270415    1.113587
         20  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
         21  |   .7203142   .1966734     3.66   0.001     .3270415    1.113587
         22  |   8.29e-16   4.35e-07     0.00   1.000    -8.70e-07    8.70e-07
         23  |    .504751   .2206156     2.29   0.026     .0636027    .9458992
         24  |   .6646843   .2061521     3.22   0.002     .2524577    1.076911
         25  |   7.76e-16   4.34e-07     0.00   1.000    -8.68e-07    8.68e-07
         26  |    .504751   .2206156     2.29   0.026     .0636027    .9458992
         27  |   .6646843   .2061521     3.22   0.002     .2524577    1.076911
         28  |   8.36e-16   5.10e-07     0.00   1.000    -1.02e-06    1.02e-06
         29  |    .504751   .2206156     2.29   0.026     .0636027    .9458992
         30  |   .6646843   .2061521     3.22   0.002     .2524577    1.076911
         32  |   .6696272   .2206981     3.03   0.004     .2283141     1.11094
         35  |   .6510839   .2305047     2.82   0.006     .1901612    1.112007
         36  |   .6226548   .2179454     2.86   0.006     .1868461    1.058463
         37  |   .6758103   .2022182     3.34   0.001       .27145    1.080171
         38  |   1.727806   1.352333     1.28   0.206    -.9763517    4.431963
         39  |   .6758103   .2022182     3.34   0.001       .27145    1.080171
         40  |   1.477806   1.067708     1.38   0.171    -.6572094    3.612821
         41  |   1.044581   .4157182     2.51   0.015     .2133007     1.87586
         42  |   .7278056   .2463023     2.95   0.004     .2352938    1.220317
         48  |   5.693113    .242668    23.46   0.000     5.207869    6.178358
         50  |   10.69311    .242668    44.06   0.000     10.20787    11.17836
         51  |   .6646843   .2061107     3.22   0.002     .2525405    1.076828
         52  |   .6370741   .2327988     2.74   0.008     .1715641    1.102584
         53  |   .6646843   .2061107     3.22   0.002     .2525405    1.076828
         54  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
         55  |   .6646843   .2061107     3.22   0.002     .2525405    1.076828
         56  |   .4778056   .1729473     2.76   0.008      .131976    .8236351
         57  |   1.711247   .2007417     8.52   0.000     1.309839    2.112655
         58  |   2.346557   .6684278     3.51   0.001     1.009953    3.683161
         61  |   3.109054   2.273493     1.37   0.176    -1.437075    7.655184
         62  |   .4060363   .2838357     1.43   0.158    -.1615282    .9736008
         63  |   .3601571   .2520074     1.43   0.158    -.1437627    .8640769
         64  |   7.88e-16   4.34e-07     0.00   1.000    -8.68e-07    8.68e-07
         65  |   2.609054   1.807864     1.44   0.154    -1.005994    6.224103
         66  |   .4060363   .2838357     1.43   0.158    -.1615282    .9736008
         67  |   2.360157   1.096967     2.15   0.035      .166635    4.553679
         68  |   8.41e-16   4.35e-07     0.00   1.000    -8.70e-07    8.70e-07
         69  |   1.524995   .2248562     6.78   0.000     1.075368    1.974623
         71  |   .8601571   .1394288     6.17   0.000     .5813519    1.138962
         72  |   8.26e-16   5.15e-07     0.00   1.000    -1.03e-06    1.03e-06
         74  |          5   4.34e-07  1.2e+07   0.000     4.999999    5.000001
         82  |   7.79e-16   5.15e-07     0.00   1.000    -1.03e-06    1.03e-06
         89  |   .6090544   .2319245     2.63   0.011     .1452927    1.072816
         90  |   .4060363   .2838357     1.43   0.158    -.1615282    .9736008
         91  |   .3601571   .2520074     1.43   0.158    -.1437627    .8640769
         92  |   8.73e-16   4.34e-07     0.00   1.000    -8.68e-07    8.68e-07
         93  |   .6090544   .2319245     2.63   0.011     .1452927    1.072816
         94  |   .4060363   .2838357     1.43   0.158    -.1615282    .9736008
         95  |   .3601571   .2520074     1.43   0.158    -.1437627    .8640769
         96  |   9.52e-16   4.34e-07     0.00   1.000    -8.68e-07    8.68e-07
         97  |   .6090544   .2319245     2.63   0.011     .1452927    1.072816
         98  |   .4060363   .2838357     1.43   0.158    -.1615282    .9736008
         99  |   .3601571   .2520074     1.43   0.158    -.1437627    .8640769
        100  |   8.35e-16   4.34e-07     0.00   1.000    -8.68e-07    8.68e-07
        101  |   1.609054   .2319245     6.94   0.000     1.145293    2.072816
        102  |          1   4.35e-07  2.3e+06   0.000     .9999991    1.000001
        103  |   1.720314   .1966734     8.75   0.000     1.327041    2.113587
        104  |          1   4.35e-07  2.3e+06   0.000     .9999991    1.000001
        105  |   3.524995   .2248562    15.68   0.000     3.075368    3.974623
        107  |   1.480209   .8527824     1.74   0.088    -.2250343    3.185453
        108  |   8.49e-16   4.35e-07     0.00   1.000    -8.70e-07    8.70e-07
        109  |   .7203142   .1966734     3.66   0.001     .3270415    1.113587
        110  |    .646141   .2129064     3.03   0.004     .2204084    1.071874
        111  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        112  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
        113  |   .7203142   .1966734     3.66   0.001     .3270415    1.113587
        114  |   3.312808   1.434962     2.31   0.024     .4434237    6.182192
        115  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        116  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
        117  |   .7203142   .1966734     3.66   0.001     .3270415    1.113587
        118  |   1.312808    .398523     3.29   0.002     .5159118    2.109704
        119  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        120  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
        137  |   .7203142   .1966734     3.66   0.001     .3270415    1.113587
        138  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
        139  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        140  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
        141  |   .7203142   .1966734     3.66   0.001     .3270415    1.113587
        142  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
        143  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        144  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
        145  |   .7203142   .1966734     3.66   0.001     .3270415    1.113587
        146  |   .7067138   .2069443     3.41   0.001     .2929032    1.120524
        147  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        148  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
        149  |   1.720314   .1966734     8.75   0.000     1.327041    2.113587
        150  |   1.206714   .5144717     2.35   0.022      .177964    2.235464
        151  |   1.524995   .2248562     6.78   0.000     1.075368    1.974623
        152  |   1.693113    .242668     6.98   0.000     1.207869    2.178358
        154  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
        157  |   8.16e-16   3.44e-07     0.00   1.000    -6.88e-07    6.88e-07
        158  |   .4151032   .2628931     1.58   0.120     -.110584    .9407904
        159  |   9.22e-16   4.20e-07     0.00   1.000    -8.40e-07    8.40e-07
        160  |   1.748437   1.229128     1.42   0.160    -.7093573     4.20623
        161  |   7.93e-16   3.44e-07     0.00   1.000    -6.88e-07    6.88e-07
        162  |   .4151032   .2628931     1.58   0.120     -.110584    .9407904
        171  |   .3465567    .346553     1.00   0.321    -.3464188    1.039532
        172  |   .4151032   .2628931     1.58   0.120     -.110584    .9407904
        173  |   9.21e-16   3.43e-07     0.00   1.000    -6.86e-07    6.86e-07
        174  |   .4151032   .2628931     1.58   0.120     -.110584    .9407904
        175  |   .3465567    .346553     1.00   0.321    -.3464188    1.039532
        176  |   .4151032   .2628931     1.58   0.120     -.110584    .9407904
        177  |          1   4.34e-07  2.3e+06   0.000     .9999991    1.000001
        178  |   .7624977   .2457106     3.10   0.003     .2711691    1.253826
        179  |          5   3.45e-07  1.5e+07   0.000     4.999999    5.000001
        182  |   2.247701   1.772935     1.27   0.210    -1.297501    5.792904
        184  |   .5810348   .2269053     2.56   0.013     .1273095     1.03476
        186  |   .9143681   .3871797     2.36   0.021     .1401544    1.688582
        196  |   .5810348   .2269053     2.56   0.013     .1273095     1.03476
        198  |   2.247701   1.882544     1.19   0.237    -1.516677     6.01208
        200  |   .5810348   .2269053     2.56   0.013     .1273095     1.03476
        202  |   3.524995   .2248562    15.68   0.000     3.075368    3.974623
        205  |   .4846058   .2652692     1.83   0.073    -.0458328    1.015044
        206  |   3.484606   1.054498     3.30   0.002     1.376007    5.593205
        207  |   1.234606   .2898253     4.26   0.000     .6550642    1.814147
        212  |   .4846058   .2652692     1.83   0.073    -.0458328    1.015044
        213  |   .4846058   .2652692     1.83   0.073    -.0458328    1.015044
        214  |   .4846058   .2652692     1.83   0.073    -.0458328    1.015044
        215  |   5.720314   .1966734    29.09   0.000     5.327041    6.113587
        217  |   .3499969   .2488617     1.41   0.165    -.1476327    .8476266
        218  |   8.71e-16   4.35e-07     0.00   1.000    -8.70e-07    8.70e-07
        219  |   3.016664   1.647822     1.83   0.072    -.2783601    6.311687
        220  |          4   4.35e-07  9.2e+06   0.000     3.999999    4.000001
        221  |   1.016664   .3399987     2.99   0.004     .3367942    1.696533
        222  |          1   4.34e-07  2.3e+06   0.000     .9999991    1.000001
        227  |   8.13e-16   4.35e-07     0.00   1.000    -8.70e-07    8.70e-07
        231  |   .3499969   .2488617     1.41   0.165    -.1476327    .8476266
        232  |   8.37e-16   4.35e-07     0.00   1.000    -8.70e-07    8.70e-07
        233  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        234  |   8.56e-16   4.35e-07     0.00   1.000    -8.70e-07    8.70e-07
        235  |   2.016664   1.610755     1.25   0.215    -1.204239    5.237567
        236  |   8.46e-16   4.35e-07     0.00   1.000    -8.70e-07    8.70e-07
        237  |          3   4.33e-07  6.9e+06   0.000     2.999999    3.000001
        239  |          4   4.34e-07  9.2e+06   0.000     3.999999    4.000001
        242  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        243  |   .3465567   .3474297     1.00   0.322     -.348172    1.041285
        244  |   .7067138   .2075629     3.40   0.001     .2916661    1.121762
        246  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        247  |   1.564371   1.350147     1.16   0.251    -1.135415    4.264157
        248  |   .7067138   .2075629     3.40   0.001     .2916661    1.121762
        250  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        251  |   .3465567   .3308124     1.05   0.299    -.3149436    1.008057
        252  |   .7067138   .2075629     3.40   0.001     .2916661    1.121762
        270  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        271  |   .2310378   .2569239     0.90   0.372    -.2827132    .7447888
        272  |   .7067138   .2075629     3.40   0.001     .2916661    1.121762
        274  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        275  |   .2310378   .2569239     0.90   0.372    -.2827132    .7447888
        276  |   .7067138   .2075629     3.40   0.001     .2916661    1.121762
        278  |   .5249954   .2248562     2.33   0.023     .0753677    .9746231
        279  |   .2310378   .2569239     0.90   0.372    -.2827132    .7447888
        280  |   .7067138   .2075629     3.40   0.001     .2916661    1.121762
        282  |   1.524995   .2248562     6.78   0.000     1.075368    1.974623
        283  |   .3465567   .3308124     1.05   0.299    -.3149436    1.008057
        284  |   2.706714    .946898     2.86   0.006     .8132742    4.600153
        288  |   .6931134    .242668     2.86   0.006     .2078687    1.178358
             |
        trt2 |  -.7203142   .1966734    -3.66   0.001    -1.113587   -.3270415
        trt3 |  -.6931134    .242668    -2.86   0.006    -1.178358   -.2078687
        trt4 |  -.5249954   .2248562    -2.33   0.023    -.9746231   -.0753677
       _cons |  -8.88e-16   4.42e-07    -0.00   1.000    -8.84e-07    8.84e-07
------------------------------------------------------------------------------

. boottest trt2, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueLocalityID, Rademacher weights:
  trt2

                           t(61) =    -3.6625
                        Prob>|t| =     0.0010

95% confidence set for null hypothesis expression: [−1.123, −.3088]

. boottest trt3, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueLocalityID, Rademacher weights:
  trt3

                           t(61) =    -2.8562
                        Prob>|t| =     0.0000

95% confidence set for null hypothesis expression: [−1.229, −.2244]

. boottest trt4, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by uniqueLocalityID, Rademacher weights:
  trt4

                           t(61) =    -2.3348
                        Prob>|t| =     0.0140

95% confidence set for null hypothesis expression: [−.9955, −.0891]

. *randomization inf: permutation test, pval
. ritest trt2 trt3 trt4 _b[trt2] _b[trt3] _b[trt4], reps($bootstrap_reps) clus
> ter(uniqueLocalityID) strata(ge01) seed(546): reg fd i.distXtrXdateFes trt2 
> trt3 trt4
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       405
-------------+----------------------------------   F(170, 234)     =      3.48
       Model |  39.1137237       170  .230080728   Prob > F        =    0.0000
    Residual |  15.4541775       234  .066043494   R-squared       =    0.7168
-------------+----------------------------------   Adj R-squared   =    0.5110
       Total |  54.5679012       404  .135069062   Root MSE        =    .25699

------------------------------------------------------------------------------
          fd | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          2  |   .1718625    .272767     0.63   0.529    -.3655304    .7092554
          3  |   .2207983   .2902993     0.76   0.448     -.351136    .7927325
          4  |  -2.35e-15   .3634377    -0.00   1.000    -.7160282    .7160282
          5  |   .2829736    .272767     1.04   0.301    -.2544193    .8203665
          6  |   .2207983   .2902993     0.76   0.448     -.351136    .7927325
          8  |   .2829736    .272767     1.04   0.301    -.2544193    .8203665
          9  |   .2207983   .2902993     0.76   0.448     -.351136    .7927325
         14  |   .2392159   .3663294     0.65   0.514    -.4825093     .960941
         17  |   .2392159   .3663294     0.65   0.514    -.4825093     .960941
         18  |   .2326852   .3669033     0.63   0.527    -.4901706     .955541
         20  |   .2392159   .3663294     0.65   0.514    -.4825093     .960941
         21  |   .2326852   .3669033     0.63   0.527    -.4901706     .955541
         22  |  -2.36e-15   .3634377    -0.00   1.000    -.7160282    .7160282
         23  |   .1718625    .272767     0.63   0.529    -.3655304    .7092554
         24  |   .2207983   .2902993     0.76   0.448     -.351136    .7927325
         25  |  -2.34e-15   .3634377    -0.00   1.000    -.7160282    .7160282
         26  |   .1718625    .272767     0.63   0.529    -.3655304    .7092554
         27  |   .2207983   .2902993     0.76   0.448     -.351136    .7927325
         28  |  -2.35e-15   .3634377    -0.00   1.000    -.7160282    .7160282
         29  |   .1718625    .272767     0.63   0.529    -.3655304    .7092554
         30  |   .2207983   .2902993     0.76   0.448     -.351136    .7927325
         32  |   .2280259   .2806796     0.81   0.417    -.3249559    .7810077
         35  |   .2240636   .2905049     0.77   0.441    -.3482757    .7964029
         36  |    .205646    .317675     0.65   0.518    -.4202225    .8315145
         37  |   .2231757   .2847165     0.78   0.434    -.3377595    .7841109
         38  |   .4142596   .2890766     1.43   0.153    -.1552656    .9837849
         39  |   .2231757   .2847165     0.78   0.434    -.3377595    .7841109
         40  |   .4142596   .2890766     1.43   0.153    -.1552656    .9837849
         41  |   .5681954   .2999864     1.89   0.059    -.0228239    1.159215
         42  |   .4142596   .2890766     1.43   0.153    -.1552656    .9837849
         48  |   1.239216   .3663294     3.38   0.001     .5174907    1.960941
         50  |   1.239216   .3663294     3.38   0.001     .5174907    1.960941
         51  |   .2207983   .2902993     0.76   0.448     -.351136    .7927325
         52  |   .2190128   .2997585     0.73   0.466    -.3715574     .809583
         53  |   .2207983   .2902993     0.76   0.448     -.351136    .7927325
         54  |   .2392159   .3180809     0.75   0.453    -.3874524    .8658841
         55  |   .2207983   .2902993     0.76   0.448     -.351136    .7927325
         56  |   .1642596   .2890766     0.57   0.570    -.4052656    .7337849
         57  |   1.234862   .2999864     4.12   0.000     .6438428    1.825881
         58  |   1.119608   .3155833     3.55   0.000     .4978604    1.741355
         61  |   .7089113    .317573     2.23   0.027     .0832438    1.334579
         62  |   .1392742   .2980811     0.47   0.641    -.4479914    .7265399
         63  |   .1163426   .2884218     0.40   0.687    -.4518926    .6845778
         64  |  -2.35e-15   .3634377    -0.00   1.000    -.7160282    .7160282
         65  |   .7089113    .317573     2.23   0.027     .0832438    1.334579
         66  |   .1392742   .2980811     0.47   0.641    -.4479914    .7265399
         67  |   .6163426   .2884218     2.14   0.034     .0481074    1.184578
         68  |  -2.35e-15   .3634377    -0.00   1.000    -.7160282    .7160282
         69  |   1.178607   .3666496     3.21   0.001     .4562508    1.900963
         71  |   .6163426   .2884218     2.14   0.034     .0481074    1.184578
         72  |  -2.34e-15   .3634377    -0.00   1.000    -.7160282    .7160282
         74  |          1   .3634377     2.75   0.006     .2839718    1.716028
         82  |  -2.35e-15   .3634377    -0.00   1.000    -.7160282    .7160282
         89  |   .2089113    .317573     0.66   0.511    -.4167562    .8345788
         90  |   .1392742   .2980811     0.47   0.641    -.4479914    .7265399
         91  |   .1163426   .2884218     0.40   0.687    -.4518926    .6845778
         92  |  -2.35e-15   .3634377    -0.00   1.000    -.7160282    .7160282
         93  |   .2089113    .317573     0.66   0.511    -.4167562    .8345788
         94  |   .1392742   .2980811     0.47   0.641    -.4479914    .7265399
         95  |   .1163426   .2884218     0.40   0.687    -.4518926    .6845778
         96  |  -2.33e-15   .3634377    -0.00   1.000    -.7160282    .7160282
         97  |   .2089113    .317573     0.66   0.511    -.4167562    .8345788
         98  |   .1392742   .2980811     0.47   0.641    -.4479914    .7265399
         99  |   .1163426   .2884218     0.40   0.687    -.4518926    .6845778
        100  |  -2.33e-15   .3634377    -0.00   1.000    -.7160282    .7160282
        101  |   1.208911    .317573     3.81   0.000     .5832438    1.834579
        102  |          1   .3634377     2.75   0.006     .2839718    1.716028
        103  |   1.232685   .3669033     3.36   0.001     .5098294    1.955541
        104  |          1   .3634377     2.75   0.006     .2839718    1.716028
        105  |   1.178607   .3666496     3.21   0.001     .4562508    1.900963
        107  |   .4884568    .298635     1.64   0.103    -.0999001    1.076814
        108  |  -2.31e-15   .3634377    -0.00   1.000    -.7160282    .7160282
        109  |   .2326852   .3669033     0.63   0.527    -.4901706     .955541
        110  |    .216836   .2995033     0.72   0.470    -.3732316    .8069035
        111  |   .1786068   .3666496     0.49   0.627    -.5437492    .9009628
        112  |   .2392159   .3663294     0.65   0.514    -.4825093     .960941
        113  |   .2326852   .3669033     0.63   0.527    -.4901706     .955541
        114  |   .8835026   .2995033     2.95   0.004     .2934351     1.47357
        115  |   .1786068   .3666496     0.49   0.627    -.5437492    .9009628
        116  |   .2392159   .3663294     0.65   0.514    -.4825093     .960941
        117  |   .2326852   .3669033     0.63   0.527    -.4901706     .955541
        118  |   .8835026   .2995033     2.95   0.004     .2934351     1.47357
        119  |   .1786068   .3666496     0.49   0.627    -.5437492    .9009628
        120  |   .2392159   .3663294     0.65   0.514    -.4825093     .960941
        137  |   .2326852   .3669033     0.63   0.527    -.4901706     .955541
        138  |   .2392159   .3663294     0.65   0.514    -.4825093     .960941
        139  |   .1786068   .3666496     0.49   0.627    -.5437492    .9009628
        140  |   .2392159   .3663294     0.65   0.514    -.4825093     .960941
        141  |   .2326852   .3669033     0.63   0.527    -.4901706     .955541
        142  |   .2392159   .3663294     0.65   0.514    -.4825093     .960941
        143  |   .1786068   .3666496     0.49   0.627    -.5437492    .9009628
        144  |   .2392159   .3663294     0.65   0.514    -.4825093     .960941
        145  |   .2326852   .3669033     0.63   0.527    -.4901706     .955541
        146  |   .2359505   .3176033     0.74   0.458    -.3897767    .8616778
        147  |   .1786068   .3666496     0.49   0.627    -.5437492    .9009628
        148  |   .2392159   .3663294     0.65   0.514    -.4825093     .960941
        149  |   1.232685   .3669033     3.36   0.001     .5098294    1.955541
        150  |   .7359505   .3176033     2.32   0.021     .1102233    1.361678
        151  |   1.178607   .3666496     3.21   0.001     .4562508    1.900963
        152  |   1.239216   .3663294     3.38   0.001     .5174907    1.960941
        154  |   .2392159   .3663294     0.65   0.514    -.4825093     .960941
        157  |  -2.34e-15   .3634377    -0.00   1.000    -.7160282    .7160282
        158  |   .1370973   .2981294     0.46   0.646    -.4502635    .7244581
        159  |  -2.32e-15   .3634377    -0.00   1.000    -.7160282    .7160282
        160  |   .4704307   .2981294     1.58   0.116    -.1169301    1.057791
        161  |  -2.37e-15   .3634377    -0.00   1.000    -.7160282    .7160282
        162  |   .1370973   .2981294     0.46   0.646    -.4502635    .7244581
        171  |   .1196079   .3155833     0.38   0.705    -.5021396    .7413554
        172  |   .1370973   .2981294     0.46   0.646    -.4502635    .7244581
        173  |  -2.35e-15   .3634377    -0.00   1.000    -.7160282    .7160282
        174  |   .1370973   .2981294     0.46   0.646    -.4502635    .7244581
        175  |   .1196079   .3155833     0.38   0.705    -.5021396    .7413554
        176  |   .1370973   .2981294     0.46   0.646    -.4502635    .7244581
        177  |          1   .3634377     2.75   0.006     .2839718    1.716028
        178  |   .5893034   .3156762     1.87   0.063    -.0326273    1.211234
        179  |          1   .3634377     2.75   0.006     .2839718    1.716028
        182  |   .5321431   .2998889     1.77   0.077    -.0586841     1.12297
        184  |   .1988098   .2998889     0.66   0.508    -.3920175    .7896371
        186  |   .5321431   .2998889     1.77   0.077    -.0586841     1.12297
        196  |   .1988098   .2998889     0.66   0.508    -.3920175    .7896371
        198  |   .5321431   .2998889     1.77   0.077    -.0586841     1.12297
        200  |   .1988098   .2998889     0.66   0.508    -.3920175    .7896371
        202  |   1.178607   .3666496     3.21   0.001     .4562508    1.900963
        205  |    .162627   .2889278     0.56   0.574    -.4066051    .7318591
        206  |    .912627   .2889278     3.16   0.002     .3433949    1.481859
        207  |    .912627   .2889278     3.16   0.002     .3433949    1.481859
        212  |    .162627   .2889278     0.56   0.574    -.4066051    .7318591
        213  |    .162627   .2889278     0.56   0.574    -.4066051    .7318591
        214  |    .162627   .2889278     0.56   0.574    -.4066051    .7318591
        215  |   1.232685   .3669033     3.36   0.001     .5098294    1.955541
        217  |   .1190712   .2984965     0.40   0.690    -.4690128    .7071552
        218  |  -2.36e-15   .3634377    -0.00   1.000    -.7160282    .7160282
        219  |   .7857378   .2984965     2.63   0.009     .1976538    1.373822
        220  |          1   .3634377     2.75   0.006     .2839718    1.716028
        221  |   .7857378   .2984965     2.63   0.009     .1976538    1.373822
        222  |          1   .3634377     2.75   0.006     .2839718    1.716028
        227  |  -2.34e-15   .3634377    -0.00   1.000    -.7160282    .7160282
        231  |   .1190712   .2984965     0.40   0.690    -.4690128    .7071552
        232  |  -2.34e-15   .3634377    -0.00   1.000    -.7160282    .7160282
        233  |   .1786068   .3184496     0.56   0.575    -.4487879    .8060015
        234  |  -2.35e-15   .3634377    -0.00   1.000    -.7160282    .7160282
        235  |   .4524045   .2984965     1.52   0.131    -.1356795    1.040489
        236  |  -2.36e-15   .3634377    -0.00   1.000    -.7160282    .7160282
        237  |          1   .3634377     2.75   0.006     .2839718    1.716028
        239  |          1   .3634377     2.75   0.006     .2839718    1.716028
        242  |   .1786068   .3184496     0.56   0.575    -.4487879    .8060015
        243  |   .1196079   .3155833     0.38   0.705    -.5021396    .7413554
        244  |   .2359505   .3176033     0.74   0.458    -.3897767    .8616778
        246  |   .1786068   .3184496     0.56   0.575    -.4487879    .8060015
        247  |    .413072   .2971405     1.39   0.166    -.1723404    .9984843
        248  |   .2359505   .3176033     0.74   0.458    -.3897767    .8616778
        250  |   .1786068   .3184496     0.56   0.575    -.4487879    .8060015
        251  |   .1196079   .3155833     0.38   0.705    -.5021396    .7413554
        252  |   .2359505   .3176033     0.74   0.458    -.3897767    .8616778
        270  |   .1786068   .3184496     0.56   0.575    -.4487879    .8060015
        271  |   .0797386   .2971405     0.27   0.789    -.5056738     .665151
        272  |   .2359505   .3176033     0.74   0.458    -.3897767    .8616778
        274  |   .1786068   .3184496     0.56   0.575    -.4487879    .8060015
        275  |   .0797386   .2971405     0.27   0.789    -.5056738     .665151
        276  |   .2359505   .3176033     0.74   0.458    -.3897767    .8616778
        278  |   .1786068   .3184496     0.56   0.575    -.4487879    .8060015
        279  |   .0797386   .2971405     0.27   0.789    -.5056738     .665151
        280  |   .2359505   .3176033     0.74   0.458    -.3897767    .8616778
        282  |   1.178607   .3184496     3.70   0.000     .5512121    1.806001
        283  |   .1196079   .3155833     0.38   0.705    -.5021396    .7413554
        284  |   1.235951   .3176033     3.89   0.000     .6102233    1.861678
        288  |   .2392159   .3663294     0.65   0.514    -.4825093     .960941
             |
        trt2 |  -.2326852   .0503092    -4.63   0.000    -.3318021   -.1335683
        trt3 |  -.2392159    .045937    -5.21   0.000    -.3297189   -.1487128
        trt4 |  -.1786068   .0484244    -3.69   0.000    -.2740103   -.0832033
       _cons |   2.33e-15   .2569893     0.00   1.000    -.5063084    .5063084
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000
Warning: 100% of the resampled realizations for _pm_1 are exactly identical to
>  original value
Warning: 100% of the resampled realizations for _pm_2 are exactly identical to
>  original value

      Command: regress fd i.distXtrXdateFes trt2 trt3 trt4
        _pm_1: trt3
        _pm_2: trt4
        _pm_3: _b[trt2]
        _pm_4: _b[trt3]
        _pm_5: _b[trt4]
  res. var(s):  trt2
   Resampling:  Permuting trt2
Clust. var(s):  uniqueLocalityID
     Clusters:  78
Strata var(s):  ge01
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_2 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_3 |  -.2326852       0    1000  0.0000  0.0000         0   .0036821
       _pm_4 |  -.2392159       0    1000  0.0000  0.0000         0   .0036821
       _pm_5 |  -.1786068       0    1000  0.0000  0.0000         0   .0036821
------------------------------------------------------------------------------
Note: Confidence intervals are with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. ritest trt2 trt3 trt4 _b[trt2] _b[trt3] _b[trt4], reps($bootstrap_reps) clus
> ter(uniqueLocalityID) strata(ge01) seed(546): reg fdamt i.distXtrXdateFes tr
> t2 trt3 trt4
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       405
-------------+----------------------------------   F(170, 234)     =      3.07
       Model |  411.807023       170  2.42239425   Prob > F        =    0.0000
    Residual |  184.834953       234   .78989296   R-squared       =    0.6902
-------------+----------------------------------   Adj R-squared   =    0.4651
       Total |  596.641975       404  1.47683657   Root MSE        =    .88876

------------------------------------------------------------------------------
       fdamt | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          2  |    .504751   .9433241     0.54   0.593    -1.353742    2.363244
          3  |   .6646843   1.003957     0.66   0.509    -1.313265    2.642634
          4  |   8.60e-16   1.256895     0.00   1.000    -2.476277    2.476277
          5  |   .9491954   .9433241     1.01   0.315     -.909298    2.807689
          6  |   .6646843   1.003957     0.66   0.509    -1.313265    2.642634
          8  |   .6158621   .9433241     0.65   0.514    -1.242631    2.474355
          9  |   .6646843   1.003957     0.66   0.509    -1.313265    2.642634
         14  |   .6931134   1.266896     0.55   0.585    -1.802866    3.189092
         17  |   .6931134   1.266896     0.55   0.585    -1.802866    3.189092
         18  |   .7203142    1.26888     0.57   0.571    -1.779575    3.220203
         20  |   .6931134   1.266896     0.55   0.585    -1.802866    3.189092
         21  |   .7203142    1.26888     0.57   0.571    -1.779575    3.220203
         22  |   8.29e-16   1.256895     0.00   1.000    -2.476277    2.476277
         23  |    .504751   .9433241     0.54   0.593    -1.353742    2.363244
         24  |   .6646843   1.003957     0.66   0.509    -1.313265    2.642634
         25  |   7.76e-16   1.256895     0.00   1.000    -2.476277    2.476277
         26  |    .504751   .9433241     0.54   0.593    -1.353742    2.363244
         27  |   .6646843   1.003957     0.66   0.509    -1.313265    2.642634
         28  |   8.36e-16   1.256895     0.00   1.000    -2.476277    2.476277
         29  |    .504751   .9433241     0.54   0.593    -1.353742    2.363244
         30  |   .6646843   1.003957     0.66   0.509    -1.313265    2.642634
         32  |   .6696272   .9706885     0.69   0.491    -1.242778    2.582033
         35  |   .6510839   1.004668     0.65   0.518    -1.328266    2.630434
         36  |   .6226548   1.098632     0.57   0.571    -1.541818    2.787128
         37  |   .6758103   .9846496     0.69   0.493    -1.264101    2.615721
         38  |   1.727806   .9997283     1.73   0.085    -.2418128    3.697424
         39  |   .6758103   .9846496     0.69   0.493    -1.264101    2.615721
         40  |   1.477806   .9997283     1.48   0.141    -.4918128    3.447424
         41  |   1.044581   1.037458     1.01   0.315    -.9993717    3.088533
         42  |   .7278056   .9997283     0.73   0.467    -1.241813    2.697424
         48  |   5.693113   1.266896     4.49   0.000     3.197134    8.189092
         50  |   10.69311   1.266896     8.44   0.000     8.197134    13.18909
         51  |   .6646843   1.003957     0.66   0.509    -1.313265    2.642634
         52  |   .6370741    1.03667     0.61   0.539    -1.405325    2.679473
         53  |   .6646843   1.003957     0.66   0.509    -1.313265    2.642634
         54  |   .6931134   1.100035     0.63   0.529    -1.474125    2.860352
         55  |   .6646843   1.003957     0.66   0.509    -1.313265    2.642634
         56  |   .4778056   .9997283     0.48   0.633    -1.491813    2.447424
         57  |   1.711247   1.037458     1.65   0.100    -.3327051      3.7552
         58  |   2.346557   1.091398     2.15   0.033     .1963354    4.496778
         61  |   3.109054   1.098279     2.83   0.005     .9452763    5.272833
         62  |   .4060363   1.030869     0.39   0.694    -1.624935    2.437007
         63  |   .3601571   .9974638     0.36   0.718       -1.605    2.325314
         64  |   7.88e-16   1.256895     0.00   1.000    -2.476277    2.476277
         65  |   2.609054   1.098279     2.38   0.018     .4452763    4.772833
         66  |   .4060363   1.030869     0.39   0.694    -1.624935    2.437007
         67  |   2.360157   .9974638     2.37   0.019     .3950001    4.325314
         68  |   8.41e-16   1.256895     0.00   1.000    -2.476277    2.476277
         69  |   1.524995   1.268003     1.20   0.230    -.9731653    4.023156
         71  |   .8601571   .9974638     0.86   0.389       -1.105    2.825314
         72  |   8.26e-16   1.256895     0.00   1.000    -2.476277    2.476277
         74  |          5   1.256895     3.98   0.000     2.523723    7.476277
         82  |   7.79e-16   1.256895     0.00   1.000    -2.476277    2.476277
         89  |   .6090544   1.098279     0.55   0.580    -1.554724    2.772833
         90  |   .4060363   1.030869     0.39   0.694    -1.624935    2.437007
         91  |   .3601571   .9974638     0.36   0.718       -1.605    2.325314
         92  |   8.73e-16   1.256895     0.00   1.000    -2.476277    2.476277
         93  |   .6090544   1.098279     0.55   0.580    -1.554724    2.772833
         94  |   .4060363   1.030869     0.39   0.694    -1.624935    2.437007
         95  |   .3601571   .9974638     0.36   0.718       -1.605    2.325314
         96  |   9.52e-16   1.256895     0.00   1.000    -2.476277    2.476277
         97  |   .6090544   1.098279     0.55   0.580    -1.554724    2.772833
         98  |   .4060363   1.030869     0.39   0.694    -1.624935    2.437007
         99  |   .3601571   .9974638     0.36   0.718       -1.605    2.325314
        100  |   8.35e-16   1.256895     0.00   1.000    -2.476277    2.476277
        101  |   1.609054   1.098279     1.47   0.144    -.5547237    3.772833
        102  |          1   1.256895     0.80   0.427    -1.476277    3.476277
        103  |   1.720314    1.26888     1.36   0.176    -.7795751    4.220203
        104  |          1   1.256895     0.80   0.427    -1.476277    3.476277
        105  |   3.524995   1.268003     2.78   0.006     1.026835    6.023156
        107  |   1.480209   1.032785     1.43   0.153    -.5545354    3.514954
        108  |   8.49e-16   1.256895     0.00   1.000    -2.476277    2.476277
        109  |   .7203142    1.26888     0.57   0.571    -1.779575    3.220203
        110  |    .646141   1.035788     0.62   0.533     -1.39452    2.686802
        111  |   .5249954   1.268003     0.41   0.679    -1.973165    3.023156
        112  |   .6931134   1.266896     0.55   0.585    -1.802866    3.189092
        113  |   .7203142    1.26888     0.57   0.571    -1.779575    3.220203
        114  |   3.312808   1.035788     3.20   0.002     1.272147    5.353469
        115  |   .5249954   1.268003     0.41   0.679    -1.973165    3.023156
        116  |   .6931134   1.266896     0.55   0.585    -1.802866    3.189092
        117  |   .7203142    1.26888     0.57   0.571    -1.779575    3.220203
        118  |   1.312808   1.035788     1.27   0.206    -.7278532    3.353469
        119  |   .5249954   1.268003     0.41   0.679    -1.973165    3.023156
        120  |   .6931134   1.266896     0.55   0.585    -1.802866    3.189092
        137  |   .7203142    1.26888     0.57   0.571    -1.779575    3.220203
        138  |   .6931134   1.266896     0.55   0.585    -1.802866    3.189092
        139  |   .5249954   1.268003     0.41   0.679    -1.973165    3.023156
        140  |   .6931134   1.266896     0.55   0.585    -1.802866    3.189092
        141  |   .7203142    1.26888     0.57   0.571    -1.779575    3.220203
        142  |   .6931134   1.266896     0.55   0.585    -1.802866    3.189092
        143  |   .5249954   1.268003     0.41   0.679    -1.973165    3.023156
        144  |   .6931134   1.266896     0.55   0.585    -1.802866    3.189092
        145  |   .7203142    1.26888     0.57   0.571    -1.779575    3.220203
        146  |   .7067138   1.098384     0.64   0.521    -1.457271    2.870698
        147  |   .5249954   1.268003     0.41   0.679    -1.973165    3.023156
        148  |   .6931134   1.266896     0.55   0.585    -1.802866    3.189092
        149  |   1.720314    1.26888     1.36   0.176    -.7795751    4.220203
        150  |   1.206714   1.098384     1.10   0.273    -.9572708    3.370698
        151  |   1.524995   1.268003     1.20   0.230    -.9731653    4.023156
        152  |   1.693113   1.266896     1.34   0.183    -.8028656    4.189092
        154  |   .6931134   1.266896     0.55   0.585    -1.802866    3.189092
        157  |   8.16e-16   1.256895     0.00   1.000    -2.476277    2.476277
        158  |   .4151032   1.031036     0.40   0.688    -1.616197    2.446403
        159  |   9.22e-16   1.256895     0.00   1.000    -2.476277    2.476277
        160  |   1.748437   1.031036     1.70   0.091    -.2828635    3.779737
        161  |   7.93e-16   1.256895     0.00   1.000    -2.476277    2.476277
        162  |   .4151032   1.031036     0.40   0.688    -1.616197    2.446403
        171  |   .3465567   1.091398     0.32   0.751    -1.803665    2.496778
        172  |   .4151032   1.031036     0.40   0.688    -1.616197    2.446403
        173  |   9.21e-16   1.256895     0.00   1.000    -2.476277    2.476277
        174  |   .4151032   1.031036     0.40   0.688    -1.616197    2.446403
        175  |   .3465567   1.091398     0.32   0.751    -1.803665    2.496778
        176  |   .4151032   1.031036     0.40   0.688    -1.616197    2.446403
        177  |          1   1.256895     0.80   0.427    -1.476277    3.476277
        178  |   .7624977   1.091719     0.70   0.486    -1.388357    2.913352
        179  |          5   1.256895     3.98   0.000     2.523723    7.476277
        182  |   2.247701   1.037121     2.17   0.031     .2044132     4.29099
        184  |   .5810348   1.037121     0.56   0.576    -1.462253    2.624323
        186  |   .9143681   1.037121     0.88   0.379     -1.12892    2.957656
        196  |   .5810348   1.037121     0.56   0.576    -1.462253    2.624323
        198  |   2.247701   1.037121     2.17   0.031     .2044132     4.29099
        200  |   .5810348   1.037121     0.56   0.576    -1.462253    2.624323
        202  |   3.524995   1.268003     2.78   0.006     1.026835    6.023156
        205  |   .4846058   .9992137     0.48   0.628    -1.483999     2.45321
        206  |   3.484606   .9992137     3.49   0.001     1.516001     5.45321
        207  |   1.234606   .9992137     1.24   0.218    -.7339988     3.20321
        212  |   .4846058   .9992137     0.48   0.628    -1.483999     2.45321
        213  |   .4846058   .9992137     0.48   0.628    -1.483999     2.45321
        214  |   .4846058   .9992137     0.48   0.628    -1.483999     2.45321
        215  |   5.720314    1.26888     4.51   0.000     3.220425    8.220203
        217  |   .3499969   1.032306     0.34   0.735    -1.683804    2.383798
        218  |   8.71e-16   1.256895     0.00   1.000    -2.476277    2.476277
        219  |   3.016664   1.032306     2.92   0.004     .9828625    5.050465
        220  |          4   1.256895     3.18   0.002     1.523723    6.476277
        221  |   1.016664   1.032306     0.98   0.326    -1.017138    3.050465
        222  |          1   1.256895     0.80   0.427    -1.476277    3.476277
        227  |   8.13e-16   1.256895     0.00   1.000    -2.476277    2.476277
        231  |   .3499969   1.032306     0.34   0.735    -1.683804    2.383798
        232  |   8.37e-16   1.256895     0.00   1.000    -2.476277    2.476277
        233  |   .5249954   1.101311     0.48   0.634    -1.644756    2.694747
        234  |   8.56e-16   1.256895     0.00   1.000    -2.476277    2.476277
        235  |   2.016664   1.032306     1.95   0.052    -.0171375    4.050465
        236  |   8.46e-16   1.256895     0.00   1.000    -2.476277    2.476277
        237  |          3   1.256895     2.39   0.018      .523723    5.476277
        239  |          4   1.256895     3.18   0.002     1.523723    6.476277
        242  |   .5249954   1.101311     0.48   0.634    -1.644756    2.694747
        243  |   .3465567   1.091398     0.32   0.751    -1.803665    2.496778
        244  |   .7067138   1.098384     0.64   0.521    -1.457271    2.870698
        246  |   .5249954   1.101311     0.48   0.634    -1.644756    2.694747
        247  |   1.564371   1.027616     1.52   0.129    -.4601905    3.588933
        248  |   .7067138   1.098384     0.64   0.521    -1.457271    2.870698
        250  |   .5249954   1.101311     0.48   0.634    -1.644756    2.694747
        251  |   .3465567   1.091398     0.32   0.751    -1.803665    2.496778
        252  |   .7067138   1.098384     0.64   0.521    -1.457271    2.870698
        270  |   .5249954   1.101311     0.48   0.634    -1.644756    2.694747
        271  |   .2310378   1.027616     0.22   0.822    -1.793524    2.255599
        272  |   .7067138   1.098384     0.64   0.521    -1.457271    2.870698
        274  |   .5249954   1.101311     0.48   0.634    -1.644756    2.694747
        275  |   .2310378   1.027616     0.22   0.822    -1.793524    2.255599
        276  |   .7067138   1.098384     0.64   0.521    -1.457271    2.870698
        278  |   .5249954   1.101311     0.48   0.634    -1.644756    2.694747
        279  |   .2310378   1.027616     0.22   0.822    -1.793524    2.255599
        280  |   .7067138   1.098384     0.64   0.521    -1.457271    2.870698
        282  |   1.524995   1.101311     1.38   0.167    -.6447558    3.694747
        283  |   .3465567   1.091398     0.32   0.751    -1.803665    2.496778
        284  |   2.706714   1.098384     2.46   0.014     .5427292    4.870698
        288  |   .6931134   1.266896     0.55   0.585    -1.802866    3.189092
             |
        trt2 |  -.7203142   .1739869    -4.14   0.000    -1.063095   -.3775332
        trt3 |  -.6931134   .1588665    -4.36   0.000    -1.006105   -.3801221
        trt4 |  -.5249954   .1674686    -3.13   0.002    -.8549342   -.1950566
       _cons |  -8.88e-16   .8887592    -0.00   1.000    -1.750992    1.750992
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000
Warning: 100% of the resampled realizations for _pm_1 are exactly identical to
>  original value
Warning: 100% of the resampled realizations for _pm_2 are exactly identical to
>  original value

      Command: regress fdamt i.distXtrXdateFes trt2 trt3 trt4
        _pm_1: trt3
        _pm_2: trt4
        _pm_3: _b[trt2]
        _pm_4: _b[trt3]
        _pm_5: _b[trt4]
  res. var(s):  trt2
   Resampling:  Permuting trt2
Clust. var(s):  uniqueLocalityID
     Clusters:  78
Strata var(s):  ge01
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_2 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_3 |  -.7203142       0    1000  0.0000  0.0000         0   .0036821
       _pm_4 |  -.6931134       0    1000  0.0000  0.0000         0   .0036821
       _pm_5 |  -.5249954       0    1000  0.0000  0.0000         0   .0036821
------------------------------------------------------------------------------
Note: Confidence intervals are with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. *mht: implement Romano-Wolf (2005) procedure, pval
. rwolf fd fdamt ihs_fdamt, indepvar(trt trt2 trt3 trt4) reps($bootstrap_reps)
>  seed(124) controls(i.distXtrXdateFes) //family (misconduct: 0/1, amount)
Bootstrap replications (1000). This may take some time.
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..................................................     50
..................................................     100
..................................................     150
..................................................     200
..................................................     250
..................................................     300
..................................................     350
..................................................     400
..................................................     450
..................................................     500
..................................................     550
..................................................     600
..................................................     650
..................................................     700
..................................................     750
..................................................     800
..................................................     850
..................................................     900
..................................................     950
..................................................     1000




Romano-Wolf step-down adjusted p-values


Independent variable:  trt
Outcome variables:   fd fdamt ihs_fdamt
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
                 fd |     0.0003             0.0240              0.0440
              fdamt |     0.0019             0.0559              0.0559
          ihs_fdamt |     0.0008             0.0410              0.0490
------------------------------------------------------------------------------


Independent variable:  trt2
Outcome variables:   fd fdamt ihs_fdamt
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
                 fd |     0.2651             0.3097              0.3696
              fdamt |     0.2445             0.2697              0.3696
          ihs_fdamt |     0.2026             0.2398              0.3287
------------------------------------------------------------------------------


Independent variable:  trt3
Outcome variables:   fd fdamt ihs_fdamt
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
                 fd |     0.1500             0.2288              0.3377
              fdamt |     0.2479             0.3956              0.3956
          ihs_fdamt |     0.1922             0.3137              0.3506
------------------------------------------------------------------------------


Independent variable:  trt4
Outcome variables:   fd fdamt ihs_fdamt
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
                 fd |          .             0.0010              0.0010
              fdamt |          .             0.0010              0.0010
          ihs_fdamt |          .             0.0010              0.0010
------------------------------------------------------------------------------



. 
. *attrition bounds-lee
. **1. [Lee Bounds]**
. foreach x of varlist trt2 trt3 trt4 {
  2.         leebounds fd `x', level(95) cieffect tight() 
  3. }

Lee (2009) treatment effect bounds

Number of obs.                     =   759
Number of selected obs.            =   405
Trimming porportion                =   0.0103
Effect 95% conf. interval          : [-0.2245  0.0275]

------------------------------------------------------------------------------
          fd | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt2         |
       lower |    -.06339    .084702    -0.75   0.454    -.2294028    .1026227
       upper |  -.0530063   .0423129    -1.25   0.210    -.1359381    .0299254
------------------------------------------------------------------------------

Lee (2009) treatment effect bounds

Number of obs.                     =   759
Number of selected obs.            =   405
Trimming porportion                =   0.0891
Effect 95% conf. interval          : [-0.2291  -0.0358]

------------------------------------------------------------------------------
          fd | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt3         |
       lower |  -.1909722    .023202    -8.23   0.000    -.2364474   -.1454971
       upper |  -.0971375   .0372902    -2.60   0.009    -.1702249   -.0240501
------------------------------------------------------------------------------

Lee (2009) treatment effect bounds

Number of obs.                     =   759
Number of selected obs.            =   405
Trimming porportion                =   0.0853
Effect 95% conf. interval          : [-0.0778  0.2327]

------------------------------------------------------------------------------
          fd | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt4         |
       lower |  -.0020351    .045346    -0.04   0.964    -.0909116    .0868414
       upper |   .0912007   .0846404     1.08   0.281    -.0746915    .2570929
------------------------------------------------------------------------------

. *
. foreach x of varlist trt2 trt3 trt4 {
  2.         leebounds fdamt `x', level(95) cieffect tight() 
  3. }

Lee (2009) treatment effect bounds

Number of obs.                     =   759
Number of selected obs.            =   405
Trimming porportion                =   0.0103
Effect 95% conf. interval          : [-1.1213  -0.0671]

------------------------------------------------------------------------------
       fdamt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt2         |
       lower |  -.3279668    .417142    -0.79   0.432     -1.14555    .4896165
       upper |  -.2760483   .1098729    -2.51   0.012    -.4913951   -.0607014
------------------------------------------------------------------------------

Lee (2009) treatment effect bounds

Number of obs.                     =   759
Number of selected obs.            =   405
Trimming porportion                =   0.0891
Effect 95% conf. interval          : [-0.5968  0.0577]

------------------------------------------------------------------------------
       fdamt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt3         |
       lower |  -.4791667   .0714422    -6.71   0.000    -.6191908   -.3391426
       upper |  -.1788957   .1437477    -1.24   0.213    -.4606359    .1028446
------------------------------------------------------------------------------

Lee (2009) treatment effect bounds

Number of obs.                     =   759
Number of selected obs.            =   405
Trimming porportion                =   0.0853
Effect 95% conf. interval          : [-0.2641  0.5679]

------------------------------------------------------------------------------
       fdamt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt4         |
       lower |  -.0325101   .1407991    -0.23   0.817    -.3084712     .243451
       upper |   .3459177    .134948     2.56   0.010     .0814244     .610411
------------------------------------------------------------------------------

. *
. **2. [Behajel et al. Bounds]**
. *denote as "all obs selected" or Not applicable here (no phone calls or repe
> at visits allowed)
. 
. 
. 
. 
. ** (1) Heterogeneity: Vendor Competition & Gender
. *Result: much effects on programs in more competitive local markets (as meas
> ure by -HHI)
. use "$dta_loc_repl/00_Raw_anon/analyzed_EndlineAuditData.dta", clear

. gen uniqueVendorID = ge03 //NOTE: uniqueVendorID = ge02, throughout

. drop _merge

. 
.         preserve

.                 use "$dta_loc_repl/01_intermediate/pct_female_MktcensusStar"
> , clear

.                 order text_ge01 text_ge02 text_ge03  

.                 keep text_ge01 text_ge02 text_ge03 HHI

.                 
.                 tempfile genderdta

.                 save    `genderdta'
file /var/folders/6g/5g5fyd2d2p98vx66fbb08m1r0000gn/T//S_35344.000002 saved
    as .dta format

.         restore

. 
. merge m:m text_ge01 text_ge02 text_ge03 using `genderdta', gen(_mg)

    Result                      Number of obs
    -----------------------------------------
    Not matched                           294
        from master                        85  (_mg==1)
        from using                        209  (_mg==2)

    Matched                             2,234  (_mg==3)
    -----------------------------------------

. sort ge*

. keep if _mg  ==3
(294 observations deleted)

. gen orig_merge = 1

. 
. 
. **NOTE: loccodee = correct, loccode=incorrect
. *br loccodex loccode loccodee
. drop _mg

. *districtName localityName localityCode
. merge m:1 ge02 using "$dta_loc_repl/00_Raw_anon/Treatments_4gps_9dist" // id
> eal merge
(label _merge already defined)

    Result                      Number of obs
    -----------------------------------------
    Not matched                             9
        from master                         0  (_merge==1)
        from using                          9  (_merge==2)

    Matched                             2,234  (_merge==3)
    -----------------------------------------

. 
. *COMPETITION
. *br loccodee localityCode_j loccode
. gen comp=-HH
(9 missing values generated)

. gen high_comp=(comp>=0.50)

. gen e_comp=MktPerLocal/populationTotal
(767 missing values generated)

. sum e_comp, d

                           e_comp
-------------------------------------------------------------
      Percentiles      Smallest
 1%     .0001635       .0001254
 5%     .0002605       .0001254
10%     .0003344       .0001254       Obs               1,476
25%     .0005169       .0001254       Sum of wgt.       1,476

50%     .0008092                      Mean           .0009811
                        Largest       Std. dev.      .0007591
75%     .0011834        .006135
90%     .0017657        .006135       Variance       5.76e-07
95%     .0024417        .006135       Skewness       3.149336
99%     .0031167        .006135       Kurtosis       19.07198

. gen high_e_comp=e_comp>=.0008718

. pwcorr e_comp comp MktPerLocal, sig

             |   e_comp     comp MktPer~l
-------------+---------------------------
      e_comp |   1.0000 
             |
             |
        comp |   0.3244   1.0000 
             |   0.0000
             |
 MktPerLocal |   0.3163   0.7547   1.0000 
             |   0.0000   0.0000
             |

. sum HHI comp, d

                             HHI
-------------------------------------------------------------
      Percentiles      Smallest
 1%      .115102              0
 5%     .1851081              0
10%     .2252066              0       Obs               2,234
25%     .3491124              0       Sum of wgt.       2,234

50%       .53125                      Mean           .5776145
                        Largest       Std. dev.      .2856864
75%     .9524093              1
90%            1              1       Variance       .0816167
95%            1              1       Skewness       .3134898
99%            1              1       Kurtosis       1.869363

                            comp
-------------------------------------------------------------
      Percentiles      Smallest
 1%           -1             -1
 5%           -1             -1
10%           -1             -1       Obs               2,234
25%    -.9524093             -1       Sum of wgt.       2,234

50%      -.53125                      Mean          -.5776145
                        Largest       Std. dev.      .2856864
75%    -.3491124              0
90%    -.2252066              0       Variance       .0816167
95%    -.1851081              0       Skewness      -.3134898
99%     -.115102              0       Kurtosis       1.869363

. 
. ** Table C.11-12 -----------------------------------------------------------
> ----
. **trim to minimize extreme influences**
. reg fd i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a i.m3
> q1 trt comp c.trt#c.comp if HHI<1 & HHI>0, r cluster(uniqueVendorID) level(9
> 5) // simple interaction

Linear regression                               Number of obs     =        228
                                                F(46, 58)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.8701
                                                Root MSE          =     .22773

                        (Std. err. adjusted for 59 clusters in uniqueVendorID)
------------------------------------------------------------------------------
             |               Robust
          fd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.2346109   .1075495    -2.18   0.033    -.4498946   -.0193272
          5  |   .4295119   .4009478     1.07   0.288    -.3730723    1.232096
          6  |    .119601   .1503318     0.80   0.430    -.1813207    .4205227
          7  |  -.2346109   .1075495    -2.18   0.033    -.4498946   -.0193272
          8  |  -.0748045   .0734548    -1.02   0.313    -.2218403    .0722314
         11  |    -.12964   .0894019    -1.45   0.152    -.3085973    .0493173
         14  |    -.12964   .0894019    -1.45   0.152    -.3085973    .0493173
         18  |    .119601   .1503318     0.80   0.430    -.1813207    .4205227
         22  |  -.2346109   .1075495    -2.18   0.033    -.4498946   -.0193272
         23  |  -.0709381   .0991457    -0.72   0.477    -.2693998    .1275235
         24  |    .119601   .1503318     0.80   0.430    -.1813207    .4205227
         26  |  -.1717292   .1335136    -1.29   0.203    -.4389856    .0955273
         27  |    .119601   .1503318     0.80   0.430    -.1813207    .4205227
         29  |  -.1469281   .1694627    -0.87   0.390    -.4861445    .1922883
         30  |    .119601   .1503318     0.80   0.430    -.1813207    .4205227
         32  |   .0197561   .1761448     0.11   0.911    -.3328359    .3723482
         33  |    .119601   .1503318     0.80   0.430    -.1813207    .4205227
         34  |  -.2346109   .1075495    -2.18   0.033    -.4498946   -.0193272
         35  |  -.0555142   .0817185    -0.68   0.500    -.2190916    .1080632
         37  |   .0329103   .0669402     0.49   0.625    -.1010852    .1669057
         38  |  -.0241617   .1634209    -0.15   0.883    -.3512842    .3029608
         39  |   .0281765   .0727929     0.39   0.700    -.1175343    .1738872
         40  |   -.021573   .1096235    -0.20   0.845    -.2410082    .1978622
         41  |   .0542138   .0870108     0.62   0.536    -.1199573     .228385
         42  |   .0084506   .1248166     0.07   0.946    -.2413971    .2582982
         51  |  -.0155613   .0422051    -0.37   0.714     -.100044    .0689215
         53  |   .0549574   .1081228     0.51   0.613    -.1614739    .2713887
         54  |   .0696005   .1330373     0.52   0.603    -.1967027    .3359036
         55  |   .0419245   .0533047     0.79   0.435    -.0647764    .1486255
         56  |   .0696005   .1330373     0.52   0.603    -.1967027    .3359036
         57  |   .8977311   .1236473     7.26   0.000     .6502242    1.145238
         58  |   .9154885   .0432722    21.16   0.000     .8288699    1.002107
         61  |   .0730001   .1242075     0.59   0.559    -.1756282    .3216284
         62  |  -.2485772   .1636027    -1.52   0.134    -.5760636    .0789093
         63  |   -.393373   .0997684    -3.94   0.000    -.5930811   -.1936649
         64  |  -.3933096   .0979996    -4.01   0.000     -.589477   -.1971422
         65  |   .1126405   .1334918     0.84   0.402    -.1545723    .3798533
         66  |   .6384852   .1254859     5.09   0.000     .3872979    .8896726
         67  |   .6595398    .142065     4.64   0.000     .3751658    .9439138
         68  |  -.3933096   .0979996    -4.01   0.000     -.589477   -.1971422
         69  |   .0002742   .1269101     0.00   0.998    -.2537641    .2543124
         71  |   .4243169   .3178508     1.33   0.187    -.2119307    1.060564
         72  |  -.3933096   .0979996    -4.01   0.000     -.589477   -.1971422
         89  |   .0723263   .1119966     0.65   0.521    -.1518593    .2965119
         90  |  -.3615148   .1254859    -2.88   0.006    -.6127021   -.1103274
         91  |  -.2974867   .1499227    -1.98   0.052    -.5975896    .0026162
         93  |   .1105468   .1414611     0.78   0.438    -.1726184    .3937121
         94  |  -.4322193   .1653071    -2.61   0.011    -.7631174   -.1013212
         95  |   -.254149   .2234909    -1.14   0.260    -.7015145    .1932166
         97  |   .0662502   .1172373     0.57   0.574    -.1684258    .3009261
         98  |  -.2485772   .1636027    -1.52   0.134    -.5760636    .0789093
         99  |  -.2894862   .2421195    -1.20   0.237     -.774141    .1951686
        102  |    .935065   .0440046    21.25   0.000     .8469802     1.02315
        103  |   1.033333   .0482744    21.41   0.000     .9367015    1.129965
        104  |   .5359858   .1543453     3.47   0.001     .2270301    .8449416
        107  |   .0333332   .0482744     0.69   0.493    -.0632985    .1299648
        109  |  -.0568683   .1178669    -0.48   0.631    -.2928046     .179068
        110  |   .0692864   .0787679     0.88   0.383    -.0883847    .2269575
        114  |   .1393537   .1135247     1.23   0.225    -.0878907     .366598
        118  |  -.0558654   .0985874    -0.57   0.573    -.2532095    .1414787
        138  |   .0785424    .084766     0.93   0.358    -.0911352    .2482199
        141  |  -.0568683   .1178669    -0.48   0.631    -.2928046     .179068
        145  |   .0138362   .0496025     0.28   0.781    -.0854539    .1131264
        146  |   .0395657   .0936193     0.42   0.674    -.1478336     .226965
        149  |   1.013836   .0496025    20.44   0.000     .9145461    1.113126
        150  |   1.055186    .126041     8.37   0.000     .8028873    1.307484
        154  |   .1393537   .1135247     1.23   0.225    -.0878907     .366598
        157  |   .0463924   .1866274     0.25   0.805    -.3271829    .4199677
        158  |  -.1015383   .2555015    -0.40   0.693    -.6129801    .4099036
        160  |   .7180725   .1628223     4.41   0.000     .3921483    1.043997
        162  |   .7180725   .1628223     4.41   0.000     .3921483    1.043997
        171  |   .0463924   .1866274     0.25   0.805    -.3271829    .4199677
        172  |    .078851   .1376922     0.57   0.569    -.1967699    .3544719
        173  |   .0463924   .1866274     0.25   0.805    -.3271829    .4199677
        174  |  -.2819275   .1628223    -1.73   0.089    -.6078517    .0439967
        175  |   .0463924   .1866274     0.25   0.805    -.3271829    .4199677
        176  |  -.2819275   .1628223    -1.73   0.089    -.6078517    .0439967
        178  |   .3984617   .4055906     0.98   0.330    -.4134161     1.21034
        180  |   .3984617   .4055906     0.98   0.330    -.4134161     1.21034
        181  |  -.3678918   .1542725    -2.38   0.020    -.6767017   -.0590818
        182  |   .1276454   .1596026     0.80   0.427     -.191834    .4471248
        183  |   .5702412   .1316668     4.33   0.000     .3066815    .8338009
        186  |   .1276454   .1596026     0.80   0.427     -.191834    .4471248
        187  |  -.4297588   .1316668    -3.26   0.002    -.6933185   -.1661991
        189  |  -.4297588   .1316668    -3.26   0.002    -.6933185   -.1661991
        191  |  -.4297588   .1316668    -3.26   0.002    -.6933185   -.1661991
        193  |  -.4297588   .1316668    -3.26   0.002    -.6933185   -.1661991
        195  |   -.363473    .126679    -2.87   0.006    -.6170485   -.1098975
        196  |   .5131814   .4736981     1.08   0.283    -.4350284    1.461391
        197  |   -.363473    .126679    -2.87   0.006    -.6170485   -.1098975
        198  |  -.1012827   .1229532    -0.82   0.413    -.3474002    .1448348
        199  |   -.363473    .126679    -2.87   0.006    -.6170485   -.1098975
        200  |  -.0221709    .171812    -0.13   0.898      -.36609    .3217481
        201  |   .5702412   .1316668     4.33   0.000     .3066815    .8338009
        202  |   .9694218   .0535263    18.11   0.000     .8622773    1.076566
        203  |   .5702412   .1316668     4.33   0.000     .3066815    .8338009
        205  |    .128859   .1567246     0.82   0.414    -.1848595    .4425774
        206  |    .628859   .4863834     1.29   0.201    -.3447433    1.602461
        207  |   .5935067   .4453924     1.33   0.188    -.2980431    1.485056
        210  |   .0275942   .0594686     0.46   0.644    -.0914452    .1466335
        211  |  -.0431104   .1159654    -0.37   0.711    -.2752404    .1890196
        212  |   .0275942   .0594686     0.46   0.644    -.0914452    .1466335
        213  |   .2301237   .1571713     1.46   0.149    -.0844889    .5447364
        214  |   .1594192   .1578922     1.01   0.317    -.1566364    .4754748
        215  |   1.027594   .0594686    17.28   0.000     .9085548    1.146634
        216  |   .2301237   .1571713     1.46   0.149    -.0844889    .5447364
        217  |   1.119827   .1167836     9.59   0.000     .8860595    1.353595
        219  |   .5227624   .4926308     1.06   0.293    -.4633453     1.50887
        221  |   .9964021   .0877986    11.35   0.000     .8206541     1.17215
        227  |   .1198273   .1167836     1.03   0.309    -.1139405    .3535951
        231  |   .1198273   .1167836     1.03   0.309    -.1139405    .3535951
        233  |  -.0035979   .0877986    -0.04   0.967    -.1793459    .1721501
        235  |   .0035873   .1490314     0.02   0.981    -.2947314     .301906
        237  |   .9964021   .0877986    11.35   0.000     .8206541     1.17215
        239  |   1.093467    .138359     7.90   0.000     .8165113    1.370423
        241  |  -.0762278   .0694999    -1.10   0.277    -.2153469    .0628912
        243  |  -.4914753   .1517918    -3.24   0.002    -.7953197   -.1876309
        244  |  -.1071897   .0590941    -1.81   0.075    -.2254793    .0110999
        245  |  -.0762278   .0694999    -1.10   0.277    -.2153469    .0628912
        247  |   .4380335   .2737105     1.60   0.115    -.1098576    .9859247
        248  |  -.0970477   .0618767    -1.57   0.122    -.2209073    .0268119
        250  |   .0077165   .0799427     0.10   0.923    -.1523061    .1677391
        251  |   .4378201   .1966139     2.23   0.030     .0442546    .8313856
        252  |  -.0970477   .0618767    -1.57   0.122    -.2209073    .0268119
        269  |  -.0762278   .0694999    -1.10   0.277    -.2153469    .0628912
        271  |  -.5621799   .1966139    -2.86   0.006    -.9557454   -.1686144
        272  |  -.1778942   .1155023    -1.54   0.129    -.4090972    .0533087
        274  |   .0077165   .0799427     0.10   0.923    -.1523061    .1677391
        275  |  -.1325643   .3044757    -0.44   0.665    -.7420387    .4769101
        276  |  -.1071897   .0590941    -1.81   0.075    -.2254793    .0110999
        278  |   .0077165   .0799427     0.10   0.923    -.1523061    .1677391
        279  |   .0492267   .1139192     0.43   0.667    -.1788073    .2772607
        280  |  -.0970477   .0618767    -1.57   0.122    -.2209073    .0268119
        283  |   .1199313   .0662263     1.81   0.075    -.0126351    .2524976
        284  |   .8322477   .1107407     7.52   0.000     .6105761    1.053919
        285  |  -.1469324   .1107686    -1.33   0.190    -.3686597     .074795
             |
      fYes_T |   .0707046    .096442     0.73   0.466    -.1223451    .2637542
        mage |  -.0046902   .0064055    -0.73   0.467    -.0175122    .0081317
    mmarried |  -.0382318   .0723971    -0.53   0.599    -.1831504    .1066868
       makan |   .0620783   .0738806     0.84   0.404    -.0858098    .2099664
mselfemplo~d |  -.0595098   .0489722    -1.22   0.229    -.1575382    .0385187
       m2q1a |  -.0007842    .013813    -0.06   0.955     -.028434    .0268655
      2.m3q1 |   .0360131   .0984808     0.37   0.716    -.1611176    .2331438
         trt |  -.5239675   .2330131    -2.25   0.028    -.9903939   -.0575411
        comp |   .7409018   .4558634     1.63   0.110     -.171608    1.653412
             |
c.trt#c.comp |  -.5950514   .5223161    -1.14   0.259    -1.640581    .4504779
             |
       _cons |   .6918237   .2323321     2.98   0.004     .2267604    1.156887
------------------------------------------------------------------------------

. reg fdamt i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a i
> .m3q1 trt comp c.trt#c.comp if HHI<1 & HHI>0, r cluster(uniqueVendorID) leve
> l(95) // simple interaction

Linear regression                               Number of obs     =        228
                                                F(46, 58)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.8580
                                                Root MSE          =     .82572

                        (Std. err. adjusted for 59 clusters in uniqueVendorID)
------------------------------------------------------------------------------
             |               Robust
       fdamt | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.2782624   .2554257    -1.09   0.280    -.7895524    .2330277
          5  |   2.294672   1.796496     1.28   0.207    -1.301405    5.890749
          6  |   .7080255   .5398399     1.31   0.195    -.3725815    1.788633
          7  |  -.2782624   .2554257    -1.09   0.280    -.7895524    .2330277
          8  |  -.2167516   .2306306    -0.94   0.351     -.678409    .2449057
         11  |  -.4239561   .2656653    -1.60   0.116    -.9557429    .1078307
         14  |  -.4239561   .2656653    -1.60   0.116    -.9557429    .1078307
         18  |   .7080255   .5398399     1.31   0.195    -.3725815    1.788633
         22  |  -.2782624   .2554257    -1.09   0.280    -.7895524    .2330277
         23  |  -.1949721    .311707    -0.63   0.534    -.8189215    .4289773
         24  |   .7080255   .5398399     1.31   0.195    -.3725815    1.788633
         26  |  -.3802455    .264618    -1.44   0.156     -.909936    .1494451
         27  |   .7080255   .5398399     1.31   0.195    -.3725815    1.788633
         29  |  -.2414485   .3241439    -0.74   0.459    -.8902929     .407396
         30  |   .7080255   .5398399     1.31   0.195    -.3725815    1.788633
         32  |  -.1584913   .2314706    -0.68   0.496    -.6218301    .3048474
         33  |   .7080255   .5398399     1.31   0.195    -.3725815    1.788633
         34  |  -.2782624   .2554257    -1.09   0.280    -.7895524    .2330277
         35  |  -.1949418   .2519425    -0.77   0.442    -.6992594    .3093759
         37  |   .1074809    .220863     0.49   0.628    -.3346244    .5495863
         38  |   .1410433    .487089     0.29   0.773    -.8339713    1.116058
         39  |   .1066164   .2606218     0.41   0.684    -.4150748    .6283075
         40  |   .1423043   .3992638     0.36   0.723    -.6569091    .9415176
         41  |   .1663771    .305679     0.54   0.588     -.445506    .7782601
         42  |   .2603109    .401755     0.65   0.520    -.5438892    1.064511
         51  |  -.0587708   .1143241    -0.51   0.609    -.2876153    .1700738
         53  |   .0732903   .2590757     0.28   0.778     -.445306    .5918867
         54  |   .4161703   .4175114     1.00   0.323    -.4195695     1.25191
         55  |   .1685928   .1989982     0.85   0.400    -.2297454    .5669309
         56  |   .4161703   .4175114     1.00   0.323    -.4195695     1.25191
         57  |   2.460312   .4247865     5.79   0.000     1.610009    3.310614
         58  |   .6615101   .1599536     4.14   0.000     .3413282     .981692
         61  |   .1639697   .3906692     0.42   0.676    -.6180396    .9459791
         62  |  -.7609224   .3332815    -2.28   0.026    -1.428058    -.093787
         63  |  -.9724607   .3580168    -2.72   0.009    -1.689109   -.2558121
         64  |  -.9335431    .332311    -2.81   0.007    -1.598736   -.2683503
         65  |   .3922395   .5321371     0.74   0.464    -.6729485    1.457428
         66  |   3.213248   .3827188     8.40   0.000     2.447153    3.979343
         67  |     3.1923   .5070873     6.30   0.000     2.177255    4.207346
         68  |  -.9335431    .332311    -2.81   0.007    -1.598736   -.2683503
         69  |   .2260849   .3954114     0.57   0.570     -.565417    1.017587
         71  |   .0813022   .3885258     0.21   0.835    -.6964167    .8590211
         72  |  -.9335431    .332311    -2.81   0.007    -1.598736   -.2683503
         89  |   .3462004   .3303021     1.05   0.299     -.314971    1.007372
         90  |  -.7867516   .3827188    -2.06   0.044    -1.552847   -.0206567
         91  |  -.6508221     .54364    -1.20   0.236    -1.739036    .4373917
         93  |   .5458049   .4762504     1.15   0.256    -.4075138    1.499124
         94  |  -1.271329   .5528728    -2.30   0.025    -2.378024   -.1646334
         95  |  -.7400699   .5482812    -1.35   0.182    -1.837574    .3574343
         97  |   .2800843   .3070066     0.91   0.365    -.3344562    .8946248
         98  |  -.7609224   .3332815    -2.28   0.026    -1.428058    -.093787
         99  |  -.9084158   .6885567    -1.32   0.192    -2.286712    .4698803
        102  |   .7494838   .1639317     4.57   0.000     .4213388    1.077629
        103  |   1.194206    .167426     7.13   0.000      .859066    1.529345
        104  |    -.41812   .5644969    -0.74   0.462    -1.548083    .7118432
        107  |   .1942055    .167426     1.16   0.251     -.140934     .529345
        109  |  -.4868147   .4362487    -1.12   0.269    -1.360061    .3864319
        110  |   .2377687   .3029385     0.78   0.436    -.3686285    .8441659
        114  |   .4967275     .44247     1.12   0.266    -.3889726    1.382427
        118  |  -.4380145   .4696813    -0.93   0.355    -1.378184    .5021548
        138  |   .1881497   .3255119     0.58   0.565    -.4634331    .8397325
        141  |  -.4868147   .4362487    -1.12   0.269    -1.360061    .3864319
        145  |  -.0022377   .1479123    -0.02   0.988    -.2983163    .2938409
        146  |   .0593027   .4272575     0.14   0.890    -.7959462    .9145516
        149  |   1.997762   .1479123    13.51   0.000     1.701684    2.293841
        150  |   1.127055   .5170776     2.18   0.033      .092012    2.162098
        154  |   .4967275     .44247     1.12   0.266    -.3889726    1.382427
        157  |   .6138959   .6308283     0.97   0.335    -.6488441    1.876636
        158  |   .0517062   .6681018     0.08   0.939    -1.285645    1.389057
        160  |   3.607572   .4024757     8.96   0.000      2.80193    4.413215
        162  |   .6075725   .4024757     1.51   0.137    -.1980702    1.413215
        171  |   .6138959   .6308283     0.97   0.335    -.6488441    1.876636
        172  |     .49584   .4603994     1.08   0.286    -.4257496     1.41743
        173  |   .6138959   .6308283     0.97   0.335    -.6488441    1.876636
        174  |  -.3924275   .4024757    -0.98   0.334     -1.19807    .4132151
        175  |   .6138959   .6308283     0.97   0.335    -.6488441    1.876636
        176  |  -.3924275   .4024757    -0.98   0.334     -1.19807    .4132151
        178  |   .5517062   .4118773     1.34   0.186    -.2727557    1.376168
        180  |   1.551706     1.3245     1.17   0.246    -1.099568     4.20298
        181  |  -1.103062   .4805519    -2.30   0.025    -2.064991   -.1411329
        182  |   .6650692   .6135126     1.08   0.283    -.5630097    1.893148
        183  |   2.974913   .4482007     6.64   0.000     2.077742    3.872085
        186  |   .6650692   .6135126     1.08   0.283    -.5630097    1.893148
        187  |  -1.025087   .4482007    -2.29   0.026    -1.922258   -.1279154
        189  |  -1.025087   .4482007    -2.29   0.026    -1.922258   -.1279154
        191  |  -1.025087   .4482007    -2.29   0.026    -1.922258   -.1279154
        193  |  -1.025087   .4482007    -2.29   0.026    -1.922258   -.1279154
        195  |  -.8217858   .3884695    -2.12   0.039    -1.599392   -.0441796
        196  |   2.551444   2.282971     1.12   0.268    -2.018418    7.121307
        197  |  -.8217858   .3884695    -2.12   0.039    -1.599392   -.0441796
        198  |  -.5621803   .4282099    -1.31   0.194    -1.419335    .2949749
        199  |  -.8217858   .3884695    -2.12   0.039    -1.599392   -.0441796
        200  |   -.190844   .6487415    -0.29   0.770    -1.489441    1.107753
        201  |   1.974913   .4482007     4.41   0.000     1.077742    2.872085
        202  |   4.922397   .1535277    32.06   0.000     4.615078    5.229716
        203  |   3.974913   .4482007     8.87   0.000     3.077742    4.872085
        205  |   .4636147   .6020174     0.77   0.444    -.7414541    1.668684
        206  |   2.463615   1.926782     1.28   0.206    -1.393258    6.320487
        207  |   2.721326   2.225356     1.22   0.226    -1.733207     7.17586
        210  |   .0511825   .1653445     0.31   0.758    -.2797905    .3821556
        211  |  -.4333944   .4095533    -1.06   0.294    -1.253204    .3864156
        212  |   .0511825   .1653445     0.31   0.758    -.2797905    .3821556
        213  |   .8760469   .5974356     1.47   0.148    -.3198503    2.071944
        214  |   .3914699   .5118422     0.76   0.447    -.6330935    1.416033
        215  |   1.051183   .1653445     6.36   0.000     .7202095    1.382156
        216  |   .8760469   .5974356     1.47   0.148    -.3198503    2.071944
        217  |   5.259803   .3125307    16.83   0.000     4.634205    5.885401
        219  |   1.925008   2.009132     0.96   0.342    -2.096707    5.946723
        221  |    1.07479   .2809707     3.83   0.000     .5123658    1.637214
        227  |    .259803   .3125307     0.83   0.409    -.3657953    .8854012
        231  |    .259803   .3125307     0.83   0.409    -.3657953    .8854012
        233  |   .0747898   .2809707     0.27   0.791    -.4876342    .6372138
        235  |  -.1722355   .4820202    -0.36   0.722    -1.137104    .7926328
        237  |    3.07479   .2809707    10.94   0.000     2.512366    3.637214
        239  |   6.409585    1.61317     3.97   0.000     3.180475    9.638695
        241  |  -.2734875   .2496231    -1.10   0.278    -.7731624    .2261874
        243  |  -1.452472   .6366478    -2.28   0.026    -2.726861   -.1780829
        244  |  -.4017021   .1995685    -2.01   0.049    -.8011818   -.0022224
        245  |  -.2734875   .2496231    -1.10   0.278    -.7731624    .2261874
        247  |    2.05974   1.331235     1.55   0.127    -.6050169    4.724497
        248  |  -.3586432   .2088243    -1.72   0.091    -.7766503     .059364
        250  |  -.0019621   .2801611    -0.01   0.994    -.5627654    .5588412
        251  |  -.9370488   .8504312    -1.10   0.275    -2.639372    .7652742
        252  |  -.3586432   .2088243    -1.72   0.091    -.7766503     .059364
        269  |  -.2734875   .2496231    -1.10   0.278    -.7731624    .2261874
        271  |  -1.937049   .8504312    -2.28   0.026    -3.639372   -.2347258
        272  |   -.886279   .4682813    -1.89   0.063    -1.823646    .0510878
        274  |  -.0019621   .2801611    -0.01   0.994    -.5627654    .5588412
        275  |  -.4264426   1.001483    -0.43   0.672    -2.431128    1.578243
        276  |  -.4017021   .1995685    -2.01   0.049    -.8011818   -.0022224
        278  |  -.0019621   .2801611    -0.01   0.994    -.5627654    .5588412
        279  |   -.051509    .353603    -0.15   0.885    -.7593222    .6563042
        280  |  -.3586432   .2088243    -1.72   0.091    -.7766503     .059364
        283  |    .433068   .2112556     2.05   0.045     .0101939    .8559421
        284  |   .1567799    .445273     0.35   0.726    -.7345309    1.048091
        285  |  -.7580645   .4368276    -1.74   0.088     -1.63247    .1163409
             |
      fYes_T |    .484577   .3614651     1.34   0.185     -.238974    1.208128
        mage |  -.0151889   .0230681    -0.66   0.513    -.0613648     .030987
    mmarried |  -.1860338   .2581258    -0.72   0.474    -.7027287    .3306611
       makan |   .1194149   .2274061     0.53   0.602    -.3357878    .5746176
mselfemplo~d |  -.2730645   .1773717    -1.54   0.129    -.6281125    .0819835
       m2q1a |  -.0245599   .0501869    -0.49   0.626    -.1250199    .0759002
      2.m3q1 |   .0503911   .2801221     0.18   0.858    -.5103342    .6111164
         trt |  -1.408091   .8829996    -1.59   0.116    -3.175607    .3594244
        comp |   2.761976   1.759054     1.57   0.122    -.7591526    6.283104
             |
c.trt#c.comp |  -2.106545   1.945338    -1.08   0.283    -6.000562    1.787473
             |
       _cons |   2.151135   1.029164     2.09   0.041     .0910386    4.211231
------------------------------------------------------------------------------

. 
. reg fd i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a i.m3
> q1 trt2 trt3 trt4 comp c.trt2#c.comp c.trt3#c.comp c.trt4#c.comp if HHI<1 & 
> HHI>0, r cluster(uniqueVendorID) level(95) // interaction

Linear regression                               Number of obs     =        228
                                                F(48, 58)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.8713
                                                Root MSE          =      .2325

                        (Std. err. adjusted for 59 clusters in uniqueVendorID)
------------------------------------------------------------------------------
             |               Robust
          fd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.2237214   .1202873    -1.86   0.068    -.4645026    .0170598
          5  |   .4369643   .4167606     1.05   0.299    -.3972726    1.271201
          6  |   .1227128   .1971032     0.62   0.536    -.2718322    .5172578
          7  |  -.2237214   .1202873    -1.86   0.068    -.4645026    .0170598
          8  |  -.0626395   .0867686    -0.72   0.473    -.2363258    .1110468
         11  |  -.1042676   .1044412    -1.00   0.322    -.3133294    .1047941
         14  |  -.1042676   .1044412    -1.00   0.322    -.3133294    .1047941
         18  |   .1227128   .1971032     0.62   0.536    -.2718322    .5172578
         22  |  -.2237214   .1202873    -1.86   0.068    -.4645026    .0170598
         23  |  -.0899642    .107039    -0.84   0.404    -.3042261    .1242976
         24  |   .1227128   .1971032     0.62   0.536    -.2718322    .5172578
         26  |  -.1636212   .1393009    -1.17   0.245    -.4424622    .1152198
         27  |   .1227128   .1971032     0.62   0.536    -.2718322    .5172578
         29  |  -.1397125   .1770029    -0.79   0.433    -.4940224    .2145974
         30  |   .1227128   .1971032     0.62   0.536    -.2718322    .5172578
         32  |   .0319551   .1887733     0.17   0.866    -.3459157    .4098259
         33  |   .1227128   .1971032     0.62   0.536    -.2718322    .5172578
         34  |  -.2237214   .1202873    -1.86   0.068    -.4645026    .0170598
         35  |  -.0439771   .0786917    -0.56   0.578    -.2014957    .1135414
         37  |   .0304369   .0764797     0.40   0.692    -.1226538    .1835277
         38  |  -.0166784   .1677341    -0.10   0.921    -.3524347     .319078
         39  |   .0323502   .0756007     0.43   0.670     -.118981    .1836815
         40  |  -.0088402   .1131652    -0.08   0.938     -.235365    .2176847
         41  |   .0574288   .0973044     0.59   0.557    -.1373471    .2522047
         42  |    .032869   .1392943     0.24   0.814    -.2459588    .3116968
         51  |  -.0073787   .0701916    -0.11   0.917    -.1478826    .1331251
         53  |     .04995   .1202088     0.42   0.679    -.1906741     .290574
         54  |   .0737126   .1356778     0.54   0.589    -.1978761    .3453013
         55  |    .044442   .0601274     0.74   0.463    -.0759161    .1648001
         56  |   .0737126   .1356778     0.54   0.589    -.1978761    .3453013
         57  |   .8881055    .138737     6.40   0.000     .6103933    1.165818
         58  |   .9497392   .0588969    16.13   0.000     .8318442    1.067634
         61  |    .087004   .1561066     0.56   0.579    -.2254773    .3994854
         62  |  -.2251588   .1828472    -1.23   0.223    -.5911672    .1408495
         63  |  -.3857986   .1088624    -3.54   0.001    -.6037104   -.1678869
         64  |  -.3817127   .1009786    -3.78   0.000    -.5838434   -.1795821
         65  |   .1207239    .158072     0.76   0.448    -.1956916    .4371393
         66  |   .6504912   .1377369     4.72   0.000     .3747809    .9262015
         67  |   .6686385   .1500182     4.46   0.000     .3683445    .9689325
         68  |  -.3817127   .1009786    -3.78   0.000    -.5838434   -.1795821
         69  |   .0203098   .1396538     0.15   0.885    -.2592376    .2998572
         71  |   .4301326   .3376175     1.27   0.208    -.2456822    1.105948
         72  |  -.3817127   .1009786    -3.78   0.000    -.5838434   -.1795821
         89  |   .0864177   .1363208     0.63   0.529     -.186458    .3592934
         90  |  -.3495088   .1377369    -2.54   0.014    -.6252191   -.0737985
         91  |  -.2839562   .1526164    -1.86   0.068    -.5894511    .0215386
         93  |   .1237137   .1516677     0.82   0.418    -.1798821    .4273096
         94  |  -.4144439    .180418    -2.30   0.025    -.7755899    -.053298
         95  |  -.2513353   .2358698    -1.07   0.291      -.72348    .2208095
         97  |   .0732712   .1405292     0.52   0.604    -.2080286     .354571
         98  |  -.2251588   .1828472    -1.23   0.223    -.5911672    .1408495
         99  |  -.2798241    .248082    -1.13   0.264    -.7764141    .2167659
        102  |   .9641263   .0461659    20.88   0.000     .8717152    1.056537
        103  |   1.023073   .0876609    11.67   0.000     .8476004    1.198545
        104  |   .5533521   .1628766     3.40   0.001     .2273191    .8793851
        107  |   .0230729   .0876609     0.26   0.793    -.1523996    .1985453
        109  |  -.0635046   .1397867    -0.45   0.651    -.3433181    .2163089
        110  |   .0734443   .0795327     0.92   0.360    -.0857576    .2326463
        114  |   .1066756   .1090283     0.98   0.332    -.1115683    .3249196
        118  |  -.0464079   .1283894    -0.36   0.719    -.3034073    .2105914
        138  |    .078633   .1276351     0.62   0.540    -.1768564    .3341224
        141  |  -.0635046   .1397867    -0.45   0.651    -.3433181    .2163089
        145  |   .0014306   .0941668     0.02   0.988    -.1870647    .1899259
        146  |   .0528868   .1208319     0.44   0.663    -.1889845    .2947581
        149  |   1.001431   .0941668    10.63   0.000     .8129353    1.189926
        150  |   1.031201   .1245655     8.28   0.000     .7818561    1.280546
        154  |   .1066756   .1090283     0.98   0.332    -.1115683    .3249196
        157  |   .0517951   .1925056     0.27   0.789    -.3335466    .4371369
        158  |  -.1106927   .2621606    -0.42   0.674    -.6354643    .4140788
        160  |    .716997     .18753     3.82   0.000     .3416149    1.092379
        162  |    .716997     .18753     3.82   0.000     .3416149    1.092379
        171  |   .0517951   .1925056     0.27   0.789    -.3335466    .4371369
        172  |   .0616176   .1529989     0.40   0.689     -.244643    .3678781
        173  |   .0517951   .1925056     0.27   0.789    -.3335466    .4371369
        174  |   -.283003     .18753    -1.51   0.137    -.6583851    .0923791
        175  |   .0517951   .1925056     0.27   0.789    -.3335466    .4371369
        176  |   -.283003     .18753    -1.51   0.137    -.6583851    .0923791
        178  |   .3893073   .4303711     0.90   0.369    -.4721741    1.250789
        180  |   .3893073   .4303711     0.90   0.369    -.4721741    1.250789
        181  |  -.3572079   .1704252    -2.10   0.040     -.698351   -.0160648
        182  |   .1306955   .2046447     0.64   0.526    -.2789454    .5403363
        183  |   .5843548   .1373568     4.25   0.000     .3094053    .8593043
        186  |   .1306955   .2046447     0.64   0.526    -.2789454    .5403363
        187  |  -.4156452   .1373568    -3.03   0.004    -.6905947   -.1406957
        189  |  -.4156452   .1373568    -3.03   0.004    -.6905947   -.1406957
        191  |  -.4156452   .1373568    -3.03   0.004    -.6905947   -.1406957
        193  |  -.4156452   .1373568    -3.03   0.004    -.6905947   -.1406957
        195  |   -.353959    .131566    -2.69   0.009    -.6173168   -.0906011
        196  |   .5319624   .5085821     1.05   0.300    -.4860753        1.55
        197  |   -.353959    .131566    -2.69   0.009    -.6173168   -.0906011
        198  |  -.0667706   .1419908    -0.47   0.640     -.350996    .2174548
        199  |   -.353959    .131566    -2.69   0.009    -.6173168   -.0906011
        200  |  -.0005052   .1830686    -0.00   0.998    -.3669567    .3659464
        201  |   .5843548   .1373568     4.25   0.000     .3094053    .8593043
        202  |   .9981645   .0625638    15.95   0.000     .8729294      1.1234
        203  |   .5843548   .1373568     4.25   0.000     .3094053    .8593043
        205  |   .1455489   .1848886     0.79   0.434    -.2245458    .5156437
        206  |   .6455489    .473241     1.36   0.178    -.3017458    1.592844
        207  |   .6130814   .4348817     1.41   0.164     -.257429    1.483592
        210  |   .0221117   .0600433     0.37   0.714    -.0980779    .1423013
        211  |  -.0428235   .1247995    -0.34   0.733    -.2926369    .2069899
        212  |   .0221117   .0600433     0.37   0.714    -.0980779    .1423013
        213  |   .2689862   .1828622     1.47   0.147    -.0970523    .6350247
        214  |   .2040511   .1827761     1.12   0.269     -.161815    .5699171
        215  |   1.022112   .0600433    17.02   0.000     .9019221    1.142301
        216  |   .2689862   .1828622     1.47   0.147    -.0970523    .6350247
        217  |   1.154308   .1447638     7.97   0.000     .8645319    1.444085
        219  |   .5332669   .4867436     1.10   0.278    -.4410562     1.50759
        221  |   .9771607   .0983812     9.93   0.000     .7802293    1.174092
        227  |   .1543083   .1447638     1.07   0.291    -.1354681    .4440846
        231  |   .1543083   .1447638     1.07   0.291    -.1354681    .4440846
        233  |  -.0228393   .0983812    -0.23   0.817    -.2197707    .1740921
        235  |   .0205008    .178084     0.12   0.909    -.3359731    .3769747
        237  |   .9771607   .0983812     9.93   0.000     .7802293    1.174092
        239  |   1.098202   .1659787     6.62   0.000     .7659596    1.430445
        241  |  -.0903499   .1095439    -0.82   0.413    -.3096258     .128926
        243  |  -.4783904   .1550426    -3.09   0.003    -.7887418    -.168039
        244  |  -.0679228   .0873675    -0.78   0.440     -.242808    .1069623
        245  |  -.0903499   .1095439    -0.82   0.413    -.3096258     .128926
        247  |   .4533015   .2730317     1.66   0.102    -.0932309    .9998339
        248  |  -.1356704   .1056224    -1.28   0.204    -.3470967    .0757558
        250  |   .0029027   .0912522     0.03   0.975    -.1797584    .1855638
        251  |   .4566744   .2074539     2.20   0.032     .0414104    .8719385
        252  |  -.1356704   .1056224    -1.28   0.204    -.3470967    .0757558
        269  |  -.0903499   .1095439    -0.82   0.413    -.3096258     .128926
        271  |  -.5433256   .2074539    -2.62   0.011    -.9585896   -.1280615
        272  |   -.132858   .1198431    -1.11   0.272      -.37275     .107034
        274  |   .0029027   .0912522     0.03   0.975    -.1797584    .1855638
        275  |  -.1133201   .3256099    -0.35   0.729    -.7650992    .5384589
        276  |  -.0679228   .0873675    -0.78   0.440     -.242808    .1069623
        278  |   .0029027   .0912522     0.03   0.975    -.1797584    .1855638
        279  |   .0795407   .1473915     0.54   0.592    -.2154954    .3745768
        280  |  -.1356704   .1056224    -1.28   0.204    -.3470967    .0757558
        283  |   .1444759   .0798104     1.81   0.075    -.0152821    .3042338
        284  |   .7993944   .1724658     4.64   0.000     .4541666    1.144622
        285  |  -.1552851   .1519015    -1.02   0.311     -.459349    .1487788
             |
      fYes_T |   .0649352   .1046052     0.62   0.537    -.1444549    .2743252
        mage |  -.0045249   .0067732    -0.67   0.507     -.018083    .0090332
    mmarried |  -.0411951   .0852583    -0.48   0.631    -.2118581    .1294679
       makan |   .0633304   .0805523     0.79   0.435    -.0979126    .2245733
mselfemplo~d |  -.0595702   .0569444    -1.05   0.300    -.1735568    .0544164
       m2q1a |  -.0028148   .0171102    -0.16   0.870    -.0370646     .031435
      2.m3q1 |   .0462298   .1077074     0.43   0.669    -.1693699    .2618295
        trt2 |  -.4579252   .2334445    -1.96   0.055    -.9252152    .0093647
        trt3 |  -.5550141   .3077053    -1.80   0.076    -1.170953    .0609249
        trt4 |  -.4878724   .2454839    -1.99   0.052    -.9792618     .003517
        comp |   .7210217   .4680841     1.54   0.129    -.2159505    1.657994
             |
      c.trt2#|
      c.comp |  -.5184121    .543872    -0.95   0.344     -1.60709     .570266
             |
      c.trt3#|
      c.comp |  -.6138332   .6672596    -0.92   0.361    -1.949498     .721832
             |
      c.trt4#|
      c.comp |  -.5571629    .534631    -1.04   0.302    -1.627343    .5130174
             |
       _cons |   .6725335   .2392498     2.81   0.007      .193623    1.151444
------------------------------------------------------------------------------

. reg fdamt i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a i
> .m3q1 trt2 trt3 trt4 comp c.trt2#c.comp c.trt3#c.comp c.trt4#c.comp if HHI<1
>  & HHI>0, r cluster(uniqueVendorID) level(95) // interaction

Linear regression                               Number of obs     =        228
                                                F(48, 58)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.8602
                                                Root MSE          =     .84028

                        (Std. err. adjusted for 59 clusters in uniqueVendorID)
------------------------------------------------------------------------------
             |               Robust
       fdamt | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.2445887   .2955033    -0.83   0.411    -.8361028    .3469254
          5  |   2.331916   1.852876     1.26   0.213    -1.377018    6.040849
          6  |   .7295763   .6770259     1.08   0.286    -.6256382    2.084791
          7  |  -.2445887   .2955033    -0.83   0.411    -.8361028    .3469254
          8  |  -.1784019   .3377613    -0.53   0.599    -.8545045    .4977008
         11  |  -.2463568   .3042493    -0.81   0.421    -.8553778    .3626643
         14  |  -.2463568   .3042493    -0.81   0.421    -.8553778    .3626643
         18  |   .7295763   .6770259     1.08   0.286    -.6256382    2.084791
         22  |  -.2445887   .2955033    -0.83   0.411    -.8361028    .3469254
         23  |  -.3684193   .4200156    -0.88   0.384    -1.209172    .4723334
         24  |   .7295763   .6770259     1.08   0.286    -.6256382    2.084791
         26  |  -.3411559   .2938583    -1.16   0.250    -.9293773    .2470654
         27  |   .7295763   .6770259     1.08   0.286    -.6256382    2.084791
         29  |  -.2101346   .3376512    -0.62   0.536     -.886017    .4657478
         30  |   .7295763   .6770259     1.08   0.286    -.6256382    2.084791
         32  |  -.0995502   .2886339    -0.34   0.731    -.6773136    .4782133
         33  |   .7295763   .6770259     1.08   0.286    -.6256382    2.084791
         34  |  -.2445887   .2955033    -0.83   0.411    -.8361028    .3469254
         35  |   -.087227   .2257511    -0.39   0.701    -.5391169    .3646628
         37  |   .0965877   .2655887     0.36   0.717    -.4350458    .6282213
         38  |   .1778106   .4843467     0.37   0.715    -.7917146    1.147336
         39  |     .13626   .2845481     0.48   0.634    -.4333249     .705845
         40  |   .2073732   .3764516     0.55   0.584    -.5461765    .9609229
         41  |   .1873291   .3474507     0.54   0.592    -.5081691    .8828273
         42  |   .3876388   .4513901     0.86   0.394    -.5159166    1.291194
         51  |  -.0257568   .2187029    -0.12   0.907    -.4635383    .4120247
         53  |   .0649849   .3026787     0.21   0.831    -.5408923    .6708621
         54  |   .4409423   .4100699     1.08   0.287    -.3799017    1.261786
         55  |   .1940809   .2340235     0.83   0.410    -.2743679    .6625298
         56  |   .4409423   .4100699     1.08   0.287    -.3799017    1.261786
         57  |   2.438876   .4748646     5.14   0.000     1.488331     3.38942
         58  |   .8307587   .2422536     3.43   0.001     .3458355    1.315682
         61  |   .2619452   .5070923     0.52   0.607    -.7531103    1.277001
         62  |  -.6999392   .3785817    -1.85   0.070    -1.457753    .0578745
         63  |  -1.013736   .4007837    -2.53   0.014    -1.815991     -.21148
         64  |  -.9451976   .3514372    -2.69   0.009    -1.648676   -.2417196
         65  |   .4510927   .5719213     0.79   0.433    -.6937322    1.595918
         66  |   3.225209    .416339     7.75   0.000     2.391816    4.058602
         67  |   3.155085   .5276139     5.98   0.000     2.098951    4.211219
         68  |  -.9451976   .3514372    -2.69   0.009    -1.648676   -.2417196
         69  |   .2884468   .4339872     0.66   0.509    -.5802729    1.157166
         71  |   .0390348   .4378589     0.09   0.929    -.8374351    .9155047
         72  |  -.9451976   .3514372    -2.69   0.009    -1.648676   -.2417196
         89  |   .4258798   .4193085     1.02   0.314    -.4134575    1.265217
         90  |  -.7747911    .416339    -1.86   0.068    -1.608184     .058602
         91  |  -.6961318   .5489081    -1.27   0.210    -1.794891    .4026272
         93  |   .5886932   .4958671     1.19   0.240    -.4038927    1.581279
         94  |  -1.240093   .6044948    -2.05   0.045    -2.450121   -.0300653
         95  |  -.7812233   .6029927    -1.30   0.200    -1.988244    .4257976
         97  |   .3311599   .4084577     0.81   0.421     -.486457    1.148777
         98  |  -.6999392   .3785817    -1.85   0.070    -1.457753    .0578745
         99  |  -.9378332   .7378687    -1.27   0.209    -2.414838    .5391715
        102  |   .8402147   .1902401     4.42   0.000     .4594078    1.221022
        103  |    1.12626    .323499     3.48   0.001     .4787062    1.773813
        104  |  -.4104997   .6063853    -0.68   0.501    -1.624312    .8033123
        107  |   .1262598    .323499     0.39   0.698    -.5212938    .7738134
        109  |  -.4874694   .5006273    -0.97   0.334    -1.489584    .5146451
        110  |   .2246505   .3171265     0.71   0.482     -.410147    .8594481
        114  |   .4358259   .4387298     0.99   0.325    -.4423872    1.314039
        118  |  -.4726285   .6437095    -0.73   0.466    -1.761153     .815896
        138  |     .16925   .5214498     0.32   0.747    -.8745451    1.213045
        141  |  -.4874694   .5006273    -0.97   0.334    -1.489584    .5146451
        145  |  -.0221674   .3010306    -0.07   0.942    -.6247456    .5804107
        146  |   .0320667   .5357363     0.06   0.952    -1.040326    1.104459
        149  |   1.977833   .3010306     6.57   0.000     1.375254    2.580411
        150  |   1.011265    .583524     1.73   0.088    -.1567853    2.179315
        154  |   .4358259   .4387298     0.99   0.325    -.4423872    1.314039
        157  |   .6405398   .6228847     1.03   0.308    -.6062994    1.887379
        158  |  -.0012833   .6468048    -0.00   0.998    -1.296004    1.293437
        160  |   3.618084   .4685582     7.72   0.000     2.680162    4.556005
        162  |   .6180836   .4685582     1.32   0.192    -.3198375    1.556005
        171  |   .6405398   .6228847     1.03   0.308    -.6062994    1.887379
        172  |   .3793498   .4838875     0.78   0.436    -.5892564    1.347956
        173  |   .6405398   .6228847     1.03   0.308    -.6062994    1.887379
        174  |  -.3819164   .4685582    -0.82   0.418    -1.319838    .5560047
        175  |   .6405398   .6228847     1.03   0.308    -.6062994    1.887379
        176  |  -.3819164   .4685582    -0.82   0.418    -1.319838    .5560047
        178  |   .4987167   .4716778     1.06   0.295    -.4454491    1.442882
        180  |   1.498717   1.442181     1.04   0.303    -1.388123    4.385556
        181  |  -1.113562   .5452372    -2.04   0.046    -2.204973   -.0221515
        182  |   .7175783    .794818     0.90   0.370    -.8734227    2.308579
        183  |   2.973929   .4664769     6.38   0.000     2.040174    3.907683
        186  |   .7175783    .794818     0.90   0.370    -.8734227    2.308579
        187  |  -1.026071   .4664769    -2.20   0.032    -1.959826   -.0923165
        189  |  -1.026071   .4664769    -2.20   0.032    -1.959826   -.0923165
        191  |  -1.026071   .4664769    -2.20   0.032    -1.959826   -.0923165
        193  |  -1.026071   .4664769    -2.20   0.032    -1.959826   -.0923165
        195  |  -.8371659   .4081734    -2.05   0.045    -1.654214    -.020118
        196  |    2.62127   2.372916     1.10   0.274    -2.128638    7.371177
        197  |  -.8371659   .4081734    -2.05   0.045    -1.654214    -.020118
        198  |   -.475039   .5105449    -0.93   0.356    -1.497006    .5469276
        199  |  -.8371659   .4081734    -2.05   0.045    -1.654214    -.020118
        200  |  -.1113814   .7003327    -0.16   0.874    -1.513249    1.290487
        201  |   1.973929   .4664769     4.23   0.000     1.040174    2.907683
        202  |   4.990263   .2238445    22.29   0.000      4.54219    5.438336
        203  |   3.973929   .4664769     8.52   0.000     3.040174    4.907683
        205  |   .5464951   .7619498     0.72   0.476    -.9787132    2.071703
        206  |   2.546495   1.840706     1.38   0.172    -1.138079    6.231069
        207  |   2.813844   2.155883     1.31   0.197    -1.501625    7.129313
        210  |   .0146084   .1654153     0.09   0.930    -.3165062    .3457231
        211  |  -.4506936    .434972    -1.04   0.304    -1.321385    .4199976
        212  |   .0146084   .1654153     0.09   0.930    -.3165062    .3457231
        213  |   1.078382   .7494735     1.44   0.156    -.4218525    2.578616
        214  |   .6130796   .6694269     0.92   0.364    -.7269239    1.953083
        215  |   1.014608   .1654153     6.13   0.000     .6834938    1.345723
        216  |   1.078382   .7494735     1.44   0.156    -.4218525    2.578616
        217  |   5.303664   .4708681    11.26   0.000     4.361119    6.246209
        219  |   1.955061   2.084468     0.94   0.352    -2.217455    6.127577
        221  |    1.07176    .322095     3.33   0.002     .4270167    1.716503
        227  |   .3036637   .4708681     0.64   0.522    -.6388812    1.246209
        231  |   .3036637   .4708681     0.64   0.522    -.6388812    1.246209
        233  |   .0717598    .322095     0.22   0.824    -.5729833    .7165029
        235  |  -.1237945   .5762562    -0.21   0.831    -1.277297    1.029708
        237  |    3.07176    .322095     9.54   0.000     2.427017    3.716503
        239  |   6.420363   1.664842     3.86   0.000     3.087819    9.752907
        241  |  -.4776609   .4377168    -1.09   0.280    -1.353846    .3985245
        243  |  -1.474002   .6606129    -2.23   0.030    -2.796363    -.151642
        244  |  -.1847596   .3312199    -0.56   0.579    -.8477683    .4782491
        245  |  -.4776609   .4377168    -1.09   0.280    -1.353846    .3985245
        247  |   2.067602   1.336785     1.55   0.127    -.6082639    4.743469
        248  |  -.4621864    .416568    -1.11   0.272    -1.296038     .371665
        250  |  -.0730259   .3239635    -0.23   0.822    -.7215092    .5754575
        251  |  -.9393043   .9058291    -1.04   0.304    -2.752518    .8739096
        252  |  -.4621864    .416568    -1.11   0.272    -1.296038     .371665
        269  |  -.4776609   .4377168    -1.09   0.280    -1.353846    .3985245
        271  |  -1.939304   .9058291    -2.14   0.036    -3.752518   -.1260904
        272  |  -.6500616   .4414444    -1.47   0.146    -1.533709    .2335854
        274  |  -.0730259   .3239635    -0.23   0.822    -.7215092    .5754575
        275  |  -.3942461   1.083823    -0.36   0.717    -2.563754    1.775262
        276  |  -.1847596   .3312199    -0.56   0.579    -.8477683    .4782491
        278  |  -.0730259   .3239635    -0.23   0.822    -.7215092    .5754575
        279  |   .0129663   .5148065     0.03   0.980    -1.017531    1.043463
        280  |  -.4621864    .416568    -1.11   0.272    -1.296038     .371665
        283  |   .4782683   .2939781     1.63   0.109    -.1101927    1.066729
        284  |   .0725116   .6973335     0.10   0.918    -1.323353    1.468376
        285  |  -.9429629   .6435255    -1.47   0.148    -2.231119    .3451934
             |
      fYes_T |    .465302   .3829457     1.22   0.229    -.3012472    1.231851
        mage |  -.0165236   .0226981    -0.73   0.470    -.0619587    .0289115
    mmarried |  -.1370455   .2517418    -0.54   0.588    -.6409614    .3668704
       makan |   .1545964   .2517509     0.61   0.542    -.3493377    .6585305
mselfemplo~d |  -.2909228   .2143387    -1.36   0.180    -.7199683    .1381227
       m2q1a |   -.038589   .0636672    -0.61   0.547    -.1660329    .0888548
      2.m3q1 |   .0584544   .3080969     0.19   0.850    -.5582686    .6751774
        trt2 |  -1.321343   .9320812    -1.42   0.162    -3.187106    .5444204
        trt3 |  -1.812489   1.199811    -1.51   0.136    -4.214171    .5891938
        trt4 |  -1.170608   .9179039    -1.28   0.207    -3.007993     .666776
        comp |   2.837344   1.788324     1.59   0.118    -.7423751    6.417064
             |
      c.trt2#|
      c.comp |   -2.06028   1.982676    -1.04   0.303    -6.029037    1.908477
             |
      c.trt3#|
      c.comp |  -2.766894   2.540623    -1.09   0.281    -7.852505    2.318716
             |
      c.trt4#|
      c.comp |  -1.825425    1.98678    -0.92   0.362    -5.802397    2.151548
             |
       _cons |   2.211056   1.040515     2.12   0.038     .1282394    4.293872
------------------------------------------------------------------------------

. 
. 
. **GENDER
. reg fd i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a i.m3
> q1 c.trt vfemale c.vfemale#c.trt, r cluster(ge02) level(95)

Linear regression                               Number of obs     =        318
                                                F(71, 85)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.7106
                                                Root MSE          =     .28685

                                  (Std. err. adjusted for 86 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
          fd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.2613554   .1333722    -1.96   0.053    -.5265351    .0038243
          5  |   .4408038   .3604278     1.22   0.225    -.2758233    1.157431
          6  |   .0483475   .1539063     0.31   0.754    -.2576594    .3543543
          7  |  -.2613554   .1333722    -1.96   0.053    -.5265351    .0038243
          8  |   .0049571   .0679092     0.07   0.942    -.1300646    .1399787
         11  |   .0346346   .0932242     0.37   0.711      -.15072    .2199892
         14  |   .0346346   .0932242     0.37   0.711      -.15072    .2199892
         18  |   .0483475   .1539063     0.31   0.754    -.2576594    .3543543
         22  |  -.2613554   .1333722    -1.96   0.053    -.5265351    .0038243
         23  |  -.0051178    .064664    -0.08   0.937    -.1336873    .1234516
         24  |   .0483475   .1539063     0.31   0.754    -.2576594    .3543543
         26  |  -.0775978   .1257866    -0.62   0.539    -.3276953    .1724998
         27  |   .0483475   .1539063     0.31   0.754    -.2576594    .3543543
         29  |  -.0931699   .1260807    -0.74   0.462    -.3438521    .1575123
         30  |   .0483475   .1539063     0.31   0.754    -.2576594    .3543543
         32  |   .1100864   .1880003     0.59   0.560    -.2637086    .4838813
         33  |   .0483475   .1539063     0.31   0.754    -.2576594    .3543543
         34  |  -.2613554   .1333722    -1.96   0.053    -.5265351    .0038243
         35  |  -.0090659    .063017    -0.14   0.886    -.1343605    .1162287
         37  |  -.0660204   .0796186    -0.83   0.409    -.2243234    .0922826
         38  |  -.0022515   .1449523    -0.02   0.988    -.2904556    .2859525
         39  |  -.0524106   .0722499    -0.73   0.470    -.1960627    .0912415
         40  |   .2119487   .2088559     1.01   0.313    -.2033128    .6272101
         41  |   .0781833   .2054217     0.38   0.704    -.3302501    .4866167
         42  |   .2703807   .2555748     1.06   0.293    -.2377705     .778532
         51  |  -.0675048   .0785254    -0.86   0.392    -.2236342    .0886247
         52  |   -.226773   .1390822    -1.63   0.107    -.5033056    .0497596
         53  |  -.0749211   .0916854    -0.82   0.416    -.2572162     .107374
         54  |  -.0208233   .1387076    -0.15   0.881    -.2966111    .2549645
         55  |  -.0531518   .0833182    -0.64   0.525    -.2188107    .1125071
         56  |   .0106291   .1134909     0.09   0.926    -.2150213    .2362795
         57  |   .8525895   .1092809     7.80   0.000     .6353098    1.069869
         58  |   .8010056   .2080564     3.85   0.000     .3873337    1.214678
         61  |  -.0985992   .0920778    -1.07   0.287    -.2816746    .0844762
         62  |  -.0946906   .0936241    -1.01   0.315    -.2808404    .0914592
         63  |  -.1700779   .1936749    -0.88   0.382    -.5551555    .2149997
         64  |  -.1425784   .1328682    -1.07   0.286     -.406756    .1215993
         65  |  -.0285545   .1467711    -0.19   0.846    -.3203747    .2632657
         66  |    .856976   .1388297     6.17   0.000     .5809453    1.133007
         67  |   .5491661   .2636375     2.08   0.040      .024984    1.073348
         68  |  -.1425784   .1328682    -1.07   0.286     -.406756    .1215993
         69  |   .0715902     .10577     0.68   0.500    -.1387088    .2818893
         71  |   .3539689   .2667394     1.33   0.188    -.1763804    .8843183
         72  |  -.1425784   .1328682    -1.07   0.286     -.406756    .1215993
         89  |   .0057004    .110511     0.05   0.959     -.214025    .2254258
         90  |  -.0535039    .137902    -0.39   0.699    -.3276901    .2206823
         91  |  -.3120296   .1552647    -2.01   0.048    -.6207374   -.0033218
         93  |   .0391313   .0910107     0.43   0.668    -.1418223    .2200849
         94  |  -.1902027   .1298332    -1.46   0.147    -.4483458    .0679405
         95  |  -.1718978     .13899    -1.24   0.220    -.4482471    .1044515
         97  |  -.0352534    .090476    -0.39   0.698     -.215144    .1446371
         98  |  -.0946906   .0936241    -1.01   0.315    -.2808404    .0914592
         99  |    -.25514    .201731    -1.26   0.209    -.6562354    .1459553
        102  |   .3629679   .6013905     0.60   0.548    -.8327576    1.558693
        103  |   1.036455   .0829845    12.49   0.000     .8714594     1.20145
        104  |   .7630643   .1491906     5.11   0.000     .4664335    1.059695
        107  |  -.0076859   .0917162    -0.08   0.933    -.1900422    .1746704
        109  |  -.1577185   .1069057    -1.48   0.144    -.3702756    .0548386
        110  |  -.0862248   .0950364    -0.91   0.367    -.2751826     .102733
        113  |  -.0883408   .0783403    -1.13   0.263    -.2441024    .0674208
        114  |  -.2203139     .22488    -0.98   0.330    -.6674355    .2268078
        117  |  -.0883408   .0783403    -1.13   0.263    -.2441024    .0674208
        118  |  -.2752865   .1186975    -2.32   0.023    -.5112889   -.0392841
        137  |   -.182698   .0976027    -1.87   0.065    -.3767584    .0113623
        138  |  -.1971043    .127995    -1.54   0.127    -.4515926     .057384
        141  |  -.2270962   .1031028    -2.20   0.030    -.4320922   -.0221002
        142  |  -.3321036    .128519    -2.58   0.011    -.5876338   -.0765734
        145  |  -.1327389   .0839665    -1.58   0.118    -.2996868    .0342089
        146  |  -.2106405   .1222203    -1.72   0.088    -.4536472    .0323662
        149  |   .3894602   .4842622     0.80   0.424    -.5733829    1.352303
        150  |   .8115668   .1649127     4.92   0.000     .4836762    1.139457
        153  |  -.0883408   .0783403    -1.13   0.263    -.2441024    .0674208
        154  |  -.0150264   .1097456    -0.14   0.891    -.2332301    .2031772
        157  |  -.3046779   .1795825    -1.70   0.093    -.6617361    .0523802
        158  |   .0473508   .1324762     0.36   0.722    -.2160474    .3107489
        160  |   .9867526   .1504248     6.56   0.000     .6876679    1.285837
        162  |   .9867526   .1504248     6.56   0.000     .6876679    1.285837
        171  |  -.3046779   .1795825    -1.70   0.093    -.6617361    .0523802
        172  |   .1079489     .11972     0.90   0.370    -.1300865    .3459843
        173  |  -.3046779   .1795825    -1.70   0.093    -.6617361    .0523802
        174  |  -.0132474   .1504248    -0.09   0.930    -.3123321    .2858373
        175  |  -.3046779   .1795825    -1.70   0.093    -.6617361    .0523802
        176  |  -.0132474   .1504248    -0.09   0.930    -.3123321    .2858373
        178  |   .5473508   .4553938     1.20   0.233    -.3580941    1.452796
        180  |   .5473508   .4553938     1.20   0.233    -.3580941    1.452796
        181  |  -.1827989   .1047987    -1.74   0.085    -.3911667    .0255689
        182  |   .0036778   .1241193     0.03   0.976    -.2431046    .2504603
        183  |    .742808   .1291895     5.75   0.000     .4859447    .9996713
        184  |   .2492991   .4070999     0.61   0.542    -.5601246    1.058723
        185  |   -.231173   .1103172    -2.10   0.039    -.4505131   -.0118329
        186  |   .0184331   .1557408     0.12   0.906    -.2912214    .3280877
        187  |   -.257192   .1291895    -1.99   0.050    -.5140553   -.0003287
        189  |   -.257192   .1291895    -1.99   0.050    -.5140553   -.0003287
        191  |   -.257192   .1291895    -1.99   0.050    -.5140553   -.0003287
        193  |   -.257192   .1291895    -1.99   0.050    -.5140553   -.0003287
        195  |  -.1958084   .1277523    -1.53   0.129    -.4498142    .0581975
        196  |   .4827275   .3833041     1.26   0.211    -.2793837    1.244839
        197  |   .1571892   .4296983     0.37   0.715    -.6971662    1.011545
        198  |  -.1443773   .0933318    -1.55   0.126    -.3299459    .0411912
        199  |  -.1761442   .1030748    -1.71   0.091    -.3810845    .0287962
        200  |   -.033336   .0994683    -0.34   0.738    -.2311056    .1644337
        201  |   .7558175   .0971504     7.78   0.000     .5626565    .9489785
        202  |   .9499799   .0695607    13.66   0.000     .8116747    1.088285
        203  |    .742808   .1291895     5.75   0.000     .4859447    .9996713
        204  |  -.0258328   .1002909    -0.26   0.797     -.225238    .1735724
        205  |  -.0531846   .0943492    -0.56   0.574    -.2407761    .1344069
        206  |    .419266   .4447653     0.94   0.349    -.4650467    1.303579
        207  |   .1675312   .2683118     0.62   0.534    -.3659445    .7010069
        210  |  -.0882883   .0955047    -0.92   0.358    -.2781772    .1016007
        211  |  -.1826456   .1115883    -1.64   0.105     -.404513    .0392219
        212  |  -.1372487   .0913028    -1.50   0.136    -.3187832    .0442857
        213  |  -.0789522   .1196412    -0.66   0.511     -.316831    .1589266
        214  |  -.1185765   .1471397    -0.81   0.423    -.4111297    .1739767
        215  |   .8627513   .0913028     9.45   0.000     .6812168    1.044286
        216  |  -.0242192   .1430423    -0.17   0.866    -.3086257    .2601872
        217  |   .3248811   .3591569     0.90   0.368    -.3892191    1.038981
        219  |   .5880439   .4269467     1.38   0.172    -.2608406    1.436928
        221  |    .480123   .4917962     0.98   0.332    -.4976996    1.457946
        227  |  -.0727928    .120162    -0.61   0.546     -.311707    .1661214
        231  |  -.0399047   .1190257    -0.34   0.738    -.2765596    .1967503
        233  |   .0055535   .0982411     0.06   0.955    -.1897762    .2008832
        235  |  -.0871042   .0983554    -0.89   0.378    -.2826611    .1084526
        237  |   1.012819   .1204928     8.41   0.000     .7732475    1.252391
        239  |   .9953944   .1029995     9.66   0.000     .7906038    1.200185
        241  |  -.0542701   .0756456    -0.72   0.475    -.2046737    .0961335
        243  |  -.4218708    .143787    -2.93   0.004    -.7077579   -.1359837
        244  |  -.0849179   .0785985    -1.08   0.283    -.2411927    .0713569
        245  |  -.0542701   .0756456    -0.72   0.475    -.2046737    .0961335
        247  |    .438905   .3064458     1.43   0.156    -.1703914    1.048201
        248  |  -.0689639   .0842412    -0.82   0.415    -.2364581    .0985302
        250  |  -.1091578   .1078605    -1.01   0.314    -.3236134    .1052977
        251  |   .0155364   .2797333     0.06   0.956    -.5406484    .5717211
        252  |  -.0134292   .0739635    -0.18   0.856    -.1604885    .1336301
        267  |  -.1328255   .0842446    -1.58   0.119    -.3003263    .0346753
        269  |  -.0542701   .0756456    -0.72   0.475    -.2046737    .0961335
        271  |  -.4102827    .198955    -2.06   0.042    -.8058586   -.0147068
        272  |  -.1396943   .0910676    -1.53   0.129    -.3207611    .0413724
        274  |  -.1091578   .1078605    -1.01   0.314    -.3236134    .1052977
        275  |  -.2200363   .1372981    -1.60   0.113    -.4930218    .0529492
        276  |  -.0849179   .0785985    -1.08   0.283    -.2411927    .0713569
        278  |  -.1091578   .1078605    -1.01   0.314    -.3236134    .1052977
        279  |  -.1825157   .1055845    -1.73   0.088    -.3924459    .0274146
        280  |  -.0134292   .0739635    -0.18   0.856    -.1604885    .1336301
        283  |  -.0881584   .0888936    -0.99   0.324    -.2649028     .088586
        284  |   .8922136   .1044572     8.54   0.000     .6845247    1.099902
        285  |  -.1486274   .0997836    -1.49   0.140    -.3470239    .0497692
        287  |  -.1328255   .0842446    -1.58   0.119    -.3003263    .0346753
             |
      fYes_T |   .0943573   .0577386     1.63   0.106    -.0204425    .2091571
        mage |   .0000866   .0048535     0.02   0.986    -.0095635    .0097367
    mmarried |  -.0334076   .0746015    -0.45   0.655    -.1817354    .1149202
       makan |  -.0832384   .0541672    -1.54   0.128    -.1909373    .0244604
mselfemplo~d |  -.0899404    .050663    -1.78   0.079    -.1906721    .0107912
       m2q1a |   .0040768   .0167137     0.24   0.808    -.0291546    .0373082
      2.m3q1 |  -.0397369   .0678677    -0.59   0.560    -.1746761    .0952023
         trt |  -.2971989   .1139023    -2.61   0.011    -.5236671   -.0707307
    vfemaleE |  -.2042942   .1383048    -1.48   0.143    -.4792812    .0706927
             |
  c.vfemaleE#|
       c.trt |   .1679631     .14682     1.14   0.256    -.1239544    .4598806
             |
       _cons |   .4200523   .1420572     2.96   0.004     .1376045    .7025001
------------------------------------------------------------------------------

. reg fdamt i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a i
> .m3q1 c.trt vfemale c.vfemale#c.trt, r cluster(ge02) level(95)

Linear regression                               Number of obs     =        318
                                                F(71, 85)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6950
                                                Root MSE          =     .97366

                                  (Std. err. adjusted for 86 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
       fdamt | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.7377416   .4078172    -1.81   0.074    -1.548591    .0731082
          5  |   1.985639   1.532967     1.30   0.199     -1.06231    5.033587
          6  |   .1439606   .4650163     0.31   0.758    -.7806165    1.068538
          7  |  -.7377416   .4078172    -1.81   0.074    -1.548591    .0731082
          8  |  -.0178932    .244316    -0.07   0.942    -.5036588    .4678724
         11  |   .0290701   .3098309     0.09   0.925    -.5869566    .6450969
         14  |   .0290701   .3098309     0.09   0.925    -.5869566    .6450969
         18  |   .1439606   .4650163     0.31   0.758    -.7806165    1.068538
         22  |  -.7377416   .4078172    -1.81   0.074    -1.548591    .0731082
         23  |  -.0292764   .2363185    -0.12   0.902    -.4991409    .4405881
         24  |   .1439606   .4650163     0.31   0.758    -.7806165    1.068538
         26  |  -.2833766   .3765915    -0.75   0.454    -1.032141    .4653882
         27  |   .1439606   .4650163     0.31   0.758    -.7806165    1.068538
         29  |  -.3046422   .3833922    -0.79   0.429    -1.066929    .4576442
         30  |   .1439606   .4650163     0.31   0.758    -.7806165    1.068538
         32  |  -.0376058   .3259778    -0.12   0.908     -.685737    .6105253
         33  |   .1439606   .4650163     0.31   0.758    -.7806165    1.068538
         34  |  -.7377416   .4078172    -1.81   0.074    -1.548591    .0731082
         35  |  -.1123897   .2097862    -0.54   0.594     -.529501    .3047215
         37  |   -.261679   .2771207    -0.94   0.348    -.8126692    .2893113
         38  |   .2149883   .7007004     0.31   0.760    -1.178192    1.608168
         39  |  -.1857295   .2484896    -0.75   0.457    -.6797934    .3083345
         40  |   .8761734   .8447309     1.04   0.303    -.8033779    2.555725
         41  |  -.1856648   .3124832    -0.59   0.554    -.8069651    .4356355
         42  |   -.253147   .4726674    -0.54   0.594    -1.192936    .6866424
         51  |  -.2534771   .2660301    -0.95   0.343    -.7824162    .2754621
         52  |   -.742557   .4376319    -1.70   0.093    -1.612687    .1275725
         53  |  -.3452457   .3159919    -1.09   0.278    -.9735223    .2830308
         54  |  -.1611953   .4746577    -0.34   0.735    -1.104942    .7825514
         55  |  -.1768355   .2894973    -0.61   0.543    -.7524336    .3987627
         56  |  -.0393636   .3669948    -0.11   0.915    -.7690476    .6903204
         57  |   1.418026   1.148261     1.23   0.220    -.8650245    3.701077
         58  |    .384116   .6229784     0.62   0.539     -.854532    1.622764
         61  |  -.4484828   .2893846    -1.55   0.125    -1.023857    .1268913
         62  |  -.3341378   .3422391    -0.98   0.332    -1.014601    .3463253
         63  |  -.5199927   .5703917    -0.91   0.365    -1.654084    .6140989
         64  |   -.407565    .427047    -0.95   0.343    -1.256649    .4415188
         65  |   -.229576   .4641129    -0.49   0.622    -1.152357    .6932048
         66  |   3.583838    .460049     7.79   0.000     2.669137    4.498538
         67  |   2.373122   1.125767     2.11   0.038     .1347944    4.611449
         68  |   -.407565    .427047    -0.95   0.343    -1.256649    .4415188
         69  |    .196188   .3367123     0.58   0.562    -.4732863    .8656623
         71  |    .006829   .3423418     0.02   0.984    -.6738382    .6874962
         72  |   -.407565    .427047    -0.95   0.343    -1.256649    .4415188
         89  |  -.0593963   .3371901    -0.18   0.861    -.7298205    .6110279
         90  |  -.1580127   .4263623    -0.37   0.712    -1.005735    .6897098
         91  |   -.951229   .5558569    -1.71   0.091    -2.056421    .1539635
         93  |   .0136633   .3000225     0.05   0.964    -.5828617    .6101884
         94  |  -.5989099     .42898    -1.40   0.166    -1.451837    .2540173
         95  |  -.5944115   .4390375    -1.35   0.179    -1.467336    .2785127
         97  |  -.1950059   .3033976    -0.64   0.522    -.7982416    .4082298
         98  |  -.3341378   .3422391    -0.98   0.332    -1.014601    .3463253
         99  |  -.8281362   .6036684    -1.37   0.174    -2.028391    .3721182
        102  |   .0904601   .8345485     0.11   0.914    -1.568846    1.749766
        103  |   1.005633   .3412781     2.95   0.004     .3270808    1.684185
        104  |     .22694   .4885855     0.46   0.643    -.7444988    1.198379
        107  |   -.135362   .3800247    -0.36   0.723    -.8909528    .6202288
        109  |  -.5483428   .3728582    -1.47   0.145    -1.289685    .1929992
        110  |  -.3001707   .3220389    -0.93   0.354    -.9404704    .3401289
        113  |  -.3149295   .2809916    -1.12   0.266    -.8736162    .2437572
        114  |  -.6802795    .677089    -1.00   0.318    -2.026514    .6659549
        117  |  -.3149295   .2809916    -1.12   0.266    -.8736162    .2437572
        118  |  -.8353177   .4270058    -1.96   0.054     -1.68432    .0136842
        137  |  -.6804246    .326805    -2.08   0.040      -1.3302   -.0306486
        138  |  -.6542237   .4094999    -1.60   0.114    -1.468419    .1599718
        141  |  -.7817561   .3716432    -2.10   0.038    -1.520682   -.0428299
        142  |  -.9503635   .4449038    -2.14   0.036    -1.834951   -.0657756
        145  |   -.416261   .3018439    -1.38   0.171    -1.016407    .1838854
        146  |  -.6724686   .3866074    -1.74   0.086    -1.441148    .0962105
        149  |   .6344047   .9888687     0.64   0.523    -1.331731    2.600541
        150  |    .411908   .5200901     0.79   0.431    -.6221704    1.445987
        153  |  -.3149295   .2809916    -1.12   0.266    -.8736162    .2437572
        154  |  -.1189998   .3882894    -0.31   0.760    -.8910231    .6530236
        157  |  -1.012699   .6766464    -1.50   0.138    -2.358053    .3326556
        158  |   .0956696   .4067506     0.24   0.815    -.7130597    .9043988
        160  |   3.966339   .4580827     8.66   0.000     3.055548     4.87713
        162  |   .9663393   .4580827     2.11   0.038     .0555482     1.87713
        171  |  -1.012699   .6766464    -1.50   0.138    -2.358053    .3326556
        172  |   .2249998   .4029989     0.56   0.578    -.5762699     1.02627
        173  |  -1.012699   .6766464    -1.50   0.138    -2.358053    .3326556
        174  |  -.0336607   .4580827    -0.07   0.942    -.9444518    .8771304
        175  |  -1.012699   .6766464    -1.50   0.138    -2.358053    .3326556
        176  |  -.0336607   .4580827    -0.07   0.942    -.9444518    .8771304
        178  |   .5956696    .535155     1.11   0.269    -.4683619    1.659701
        180  |    1.59567   1.425377     1.12   0.266    -1.238362    4.429701
        181  |  -.5895594   .3770595    -1.56   0.122    -1.339255    .1601359
        182  |  -.0538585   .4598551    -0.12   0.907    -.9681737    .8604567
        183  |   3.280321   .3986076     8.23   0.000     2.487782     4.07286
        184  |   .9442096   1.586474     0.60   0.553    -2.210126    4.098545
        185  |  -.7919583   .3868167    -2.05   0.044    -1.561053   -.0228631
        186  |  -.0155016    .605313    -0.03   0.980    -1.219026    1.188023
        187  |  -.7196791   .3986076    -1.81   0.075    -1.512218    .0728596
        189  |  -.7196791   .3986076    -1.81   0.075    -1.512218    .0728596
        191  |  -.7196791   .3986076    -1.81   0.075    -1.512218    .0728596
        193  |  -.7196791   .3986076    -1.81   0.075    -1.512218    .0728596
        195  |  -.5534198   .3936007    -1.41   0.163    -1.336004    .2291639
        196  |   2.397368   2.066375     1.16   0.249    -1.711139    6.505874
        197  |  -.1777676   .5585389    -0.32   0.751    -1.288293    .9327574
        198  |  -.5621085   .3393449    -1.66   0.101    -1.236817    .1126001
        199  |   -.511101   .3377791    -1.51   0.134    -1.182696    .1604943
        200  |   -.180921   .3321059    -0.54   0.587    -.8412364    .4793944
        201  |   2.244181   .3514054     6.39   0.000     1.545493    2.942869
        202  |   4.803387   .2710005    17.72   0.000     4.264565    5.342208
        203  |   4.280321   .3986076    10.74   0.000     3.487782     5.07286
        204  |  -.1305723   .3081185    -0.42   0.673    -.7431945    .4820499
        205  |  -.2123082   .3309733    -0.64   0.523    -.8703717    .4457553
        206  |   1.650254   1.835547     0.90   0.371    -1.999305    5.299812
        207  |   .8866934   1.393763     0.64   0.526    -1.884481    3.657868
        210  |  -.2993452   .3507119    -0.85   0.396    -.9966545     .397964
        211  |  -.6648403   .4162119    -1.60   0.114    -1.492381    .1627006
        212  |  -.4657648   .3283741    -1.42   0.160    -1.118661     .187131
        213  |  -.3660743   .4152353    -0.88   0.380    -1.191673    .4595246
        214  |   -.599223   .5310047    -1.13   0.262    -1.655003    .4565567
        215  |   .5342352   .3283741     1.63   0.107    -.1186606    1.187131
        216  |  -.2337279   .5090934    -0.46   0.647    -1.245942    .7784861
        217  |     1.5606   1.781334     0.88   0.383    -1.981168    5.102369
        219  |   2.278004   1.722774     1.32   0.190    -1.147334    5.703341
        221  |   .3390226   .5265881     0.64   0.521    -.7079757    1.386021
        227  |  -.3721264   .4208324    -0.88   0.379    -1.208854    .4646011
        231  |   -.227898   .4375178    -0.52   0.604    -1.097801    .6420046
        233  |    -.05515   .3353681    -0.16   0.870    -.7219518    .6116517
        235  |  -.4303817   .3483626    -1.24   0.220     -1.12302    .2622565
        237  |   1.968564   .8819891     2.23   0.028     .2149329    3.722194
        239  |   5.886996   1.122072     5.25   0.000     3.656016    8.117975
        241  |  -.2225363   .2852516    -0.78   0.437    -.7896928    .3446202
        243  |  -1.262798   .5573405    -2.27   0.026    -2.370941   -.1546559
        244  |  -.2596927   .2902712    -0.89   0.373    -.8368296    .3174442
        245  |  -.2225363   .2852516    -0.78   0.437    -.7896928    .3446202
        247  |   1.969554   1.320599     1.49   0.140    -.6561506    4.595259
        248  |  -.2361207   .2797417    -0.84   0.401     -.792322    .3200807
        250  |   -.405242   .3297499    -1.23   0.222    -1.060873    .2503892
        251  |  -.6442046   .4377187    -1.47   0.145    -1.514507    .2260974
        252  |   -.084245   .2534649    -0.33   0.740    -.5882011     .419711
        267  |  -.4241213   .3054151    -1.39   0.169    -1.031368    .1831256
        269  |  -.2225363   .2852516    -0.78   0.437    -.7896928    .3446202
        271  |  -1.254246   .6509983    -1.93   0.057    -2.548605    .0401128
        272  |  -.4968841   .3271774    -1.52   0.133      -1.1474    .1536322
        274  |   -.405242   .3297499    -1.23   0.222    -1.060873    .2503892
        275  |  -.6914111   .4569349    -1.51   0.134     -1.59992     .217098
        276  |  -.2596927   .2902712    -0.89   0.373    -.8368296    .3174442
        278  |   -.405242   .3297499    -1.23   0.222    -1.060873    .2503892
        279  |  -.6530498   .3701688    -1.76   0.081    -1.389045     .082945
        280  |   -.084245   .2534649    -0.33   0.740    -.5882011     .419711
        283  |  -.2875548   .3112292    -0.92   0.358    -.9063617    .3312522
        284  |   .5502599   .3285054     1.68   0.098    -.1028969    1.203417
        285  |  -.5880313   .3579037    -1.64   0.104     -1.29964    .1235771
        287  |  -.4241213   .3054151    -1.39   0.169    -1.031368    .1831256
             |
      fYes_T |   .3654951   .1883378     1.94   0.056    -.0089709    .7399611
        mage |   .0078603   .0163791     0.48   0.633    -.0247058    .0404264
    mmarried |  -.1207937   .2354933    -0.51   0.609    -.5890176    .3474301
       makan |  -.2668095   .1890792    -1.41   0.162    -.6427496    .1091306
mselfemplo~d |  -.3281554   .2002774    -1.64   0.105    -.7263605    .0700498
       m2q1a |   .0102022   .0448257     0.23   0.821    -.0789233    .0993277
      2.m3q1 |  -.0771901   .2082938    -0.37   0.712    -.4913341    .3369538
         trt |  -.8590813   .3540943    -2.43   0.017    -1.563116   -.1550469
    vfemaleE |  -.6166885   .4572974    -1.35   0.181    -1.525918    .2925412
             |
  c.vfemaleE#|
       c.trt |   .5279011   .4453633     1.19   0.239    -.3576005    1.413403
             |
       _cons |   1.097732   .5121387     2.14   0.035     .0794628    2.116001
------------------------------------------------------------------------------

. 
. reg fd i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a i.m3
> q1 trt2 trt3 trt4 vfemale c.trt2#c.vfemale c.trt3#c.vfemale c.trt4#c.vfemale
> , r cluster(ge02) level(95)

Linear regression                               Number of obs     =        318
                                                F(75, 85)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.7128
                                                Root MSE          =      .2894

                                  (Std. err. adjusted for 86 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
          fd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.2358344   .1518365    -1.55   0.124     -.537726    .0660572
          5  |   .4596252   .3737069     1.23   0.222    -.2834043    1.202655
          6  |   .0766182    .212075     0.36   0.719    -.3450439    .4982802
          7  |  -.2358344   .1518365    -1.55   0.124     -.537726    .0660572
          8  |   .0255401   .0880616     0.29   0.773      -.14955    .2006303
         11  |   .0919978   .1281899     0.72   0.475    -.1628781    .3468738
         14  |   .0919978   .1281899     0.72   0.475    -.1628781    .3468738
         18  |   .0766182    .212075     0.36   0.719    -.3450439    .4982802
         22  |  -.2358344   .1518365    -1.55   0.124     -.537726    .0660572
         23  |   .0352929    .099004     0.36   0.722    -.1615535    .2321393
         24  |   .0766182    .212075     0.36   0.719    -.3450439    .4982802
         26  |  -.0490891   .1452373    -0.34   0.736    -.3378597    .2396816
         27  |   .0766182    .212075     0.36   0.719    -.3450439    .4982802
         29  |  -.0714596   .1330369    -0.54   0.593    -.3359726    .1930534
         30  |   .0766182    .212075     0.36   0.719    -.3450439    .4982802
         32  |   .1337063   .2016196     0.66   0.509    -.2671674    .5345801
         33  |   .0766182    .212075     0.36   0.719    -.3450439    .4982802
         34  |  -.2358344   .1518365    -1.55   0.124     -.537726    .0660572
         35  |  -.0023623   .0805413    -0.03   0.977    -.1624999    .1577754
         37  |   -.066687   .1094046    -0.61   0.544    -.2842126    .1508386
         38  |   .0066589     .15617     0.04   0.966    -.3038489    .3171667
         39  |  -.0458181    .102435    -0.45   0.656    -.2494863    .1578501
         40  |   .2278328   .1999994     1.14   0.258    -.1698196    .6254853
         41  |   .0700128   .2022607     0.35   0.730    -.3321356    .4721612
         42  |   .2896641   .2478341     1.17   0.246    -.2030966    .7824247
         51  |    -.07138   .1045971    -0.68   0.497    -.2793471     .136587
         52  |   -.195142   .1713515    -1.14   0.258    -.5358346    .1455507
         53  |   -.089838   .1132411    -0.79   0.430    -.3149917    .1353157
         54  |  -.0058363    .133914    -0.04   0.965    -.2720932    .2604206
         55  |  -.0444468   .1158386    -0.38   0.702    -.2747651    .1858714
         56  |   .0262516   .1122602     0.23   0.816    -.1969518    .2494549
         57  |   .8225814   .1359268     6.05   0.000     .5523225     1.09284
         58  |    .831362    .242333     3.43   0.001     .3495391    1.313185
         61  |  -.0541108   .1149636    -0.47   0.639    -.2826891    .1744676
         62  |  -.0824233   .1197244    -0.69   0.493    -.3204674    .1556209
         63  |  -.1444692   .2235886    -0.65   0.520    -.5890233    .3000849
         64  |  -.1174256   .1503134    -0.78   0.437    -.4162889    .1814377
         65  |   .0195874   .1733513     0.11   0.910    -.3250815    .3642563
         66  |    .888882   .1454865     6.11   0.000     .5996158    1.178148
         67  |   .5722988   .2653918     2.16   0.034     .0446287    1.099969
         68  |  -.1174256   .1503134    -0.78   0.437    -.4162889    .1814377
         69  |   .0831291   .1146875     0.72   0.471    -.1449005    .3111586
         71  |   .3636147   .2993563     1.21   0.228    -.2315859    .9588154
         72  |  -.1174256   .1503134    -0.78   0.437    -.4162889    .1814377
         89  |   .0398636   .1313623     0.30   0.762    -.2213199    .3010471
         90  |  -.0216275   .1436096    -0.15   0.881     -.307162     .263907
         91  |   -.290316   .1663028    -1.75   0.084    -.6209704    .0403385
         93  |   .0843358   .1063456     0.79   0.430    -.1271077    .2957793
         94  |  -.1592499   .1392999    -1.14   0.256    -.4362154    .1177157
         95  |  -.1596326   .1509722    -1.06   0.293    -.4598058    .1405406
         97  |   -.001573   .1091986    -0.01   0.989    -.2186891    .2155431
         98  |  -.0824233   .1197244    -0.69   0.493    -.3204674    .1556209
         99  |  -.2474316   .2031188    -1.22   0.227    -.6512862    .1564231
        102  |    .376218   .5930505     0.63   0.528    -.8029253    1.555361
        103  |    1.03616   .1366785     7.58   0.000     .7644062    1.307913
        104  |   .7863107   .1654127     4.75   0.000      .457426    1.115195
        107  |   .0040015   .1238802     0.03   0.974    -.2423055    .2503085
        109  |  -.1784409   .1447211    -1.23   0.221    -.4661853    .1093035
        110  |  -.0813053    .112546    -0.72   0.472    -.3050768    .1424663
        113  |  -.1211218   .1311231    -0.92   0.358    -.3818298    .1395861
        114  |  -.2158122   .2279732    -0.95   0.346    -.6690839    .2374595
        117  |  -.1211218   .1311231    -0.92   0.358    -.3818298    .1395861
        118  |  -.2730964   .1298909    -2.10   0.038    -.5313543   -.0148386
        137  |  -.2173855    .143517    -1.51   0.134    -.5027357    .0679647
        138  |  -.1951066   .1384061    -1.41   0.162    -.4702949    .0800818
        141  |    -.23576   .1609405    -1.46   0.147    -.5557528    .0842328
        142  |  -.3258785   .1428178    -2.28   0.025    -.6098386   -.0419184
        145  |  -.1394964    .151487    -0.92   0.360     -.440693    .1617003
        146  |  -.1985577   .1377825    -1.44   0.153    -.4725063    .0753908
        149  |   .3696909   .5142331     0.72   0.474    -.6527423    1.392124
        150  |    .821641   .1758708     4.67   0.000     .4719628    1.171319
        153  |  -.1211218   .1311231    -0.92   0.358    -.3818298    .1395861
        154  |  -.0082616   .1455261    -0.06   0.955    -.2976065    .2810832
        157  |  -.2889662   .1883217    -1.53   0.129    -.6634002    .0854678
        158  |   .0920818   .1455164     0.63   0.529    -.1972437    .3814073
        160  |   1.021196    .152124     6.71   0.000     .7187323    1.323659
        162  |   1.021196    .152124     6.71   0.000     .7187323    1.323659
        171  |  -.2889662   .1883217    -1.53   0.129    -.6634002    .0854678
        172  |   .1629681   .1408215     1.16   0.250    -.1170227    .4429588
        173  |  -.2889662   .1883217    -1.53   0.129    -.6634002    .0854678
        174  |   .0211956    .152124     0.14   0.890    -.2812677    .3236588
        175  |  -.2889662   .1883217    -1.53   0.129    -.6634002    .0854678
        176  |   .0211956    .152124     0.14   0.890    -.2812677    .3236588
        178  |   .5920818   .4531006     1.31   0.195    -.3088036    1.492967
        180  |   .5920818   .4531006     1.31   0.195    -.3088036    1.492967
        181  |  -.1596775    .124836    -1.28   0.204    -.4078849    .0885299
        182  |   .0413119   .1323409     0.31   0.756    -.2218171     .304441
        183  |    .766168   .1473863     5.20   0.000     .4731246    1.059211
        184  |   .2930946   .4422197     0.66   0.509    -.5861566    1.172346
        185  |   -.206145   .1352178    -1.52   0.131    -.4749942    .0627042
        186  |   .0427555   .1436504     0.30   0.767    -.2428601    .3283711
        187  |   -.233832   .1473863    -1.59   0.116    -.5268754    .0592115
        189  |   -.233832   .1473863    -1.59   0.116    -.5268754    .0592115
        191  |   -.233832   .1473863    -1.59   0.116    -.5268754    .0592115
        193  |   -.233832   .1473863    -1.59   0.116    -.5268754    .0592115
        195  |   -.173521   .1436721    -1.21   0.230    -.4591796    .1121377
        196  |   .4777311   .3978472     1.20   0.233    -.3132957    1.268758
        197  |   .1810256     .44427     0.41   0.685    -.7023021    1.064353
        198  |  -.1470131     .11602    -1.27   0.209     -.377692    .0836658
        199  |  -.1523078   .1223352    -1.25   0.217     -.395543    .0909275
        200  |  -.0057693   .1342352    -0.04   0.966    -.2726649    .2611262
        201  |   .7800115   .1200459     6.50   0.000      .541328    1.018695
        202  |   .9492506   .0976566     9.72   0.000     .7550831    1.143418
        203  |    .766168   .1473863     5.20   0.000     .4731246    1.059211
        204  |   .0384248   .1503626     0.26   0.799    -.2605364     .337386
        205  |  -.0397644   .1077898    -0.37   0.713    -.2540794    .1745505
        206  |   .4357482   .4556442     0.96   0.342    -.4701945    1.341691
        207  |   .1675395    .291947     0.57   0.568    -.4129295    .7480084
        210  |  -.0795989   .1043822    -0.76   0.448    -.2871387    .1279408
        211  |  -.1758626   .1199553    -1.47   0.146    -.4143659    .0626408
        212  |  -.1093544   .1168132    -0.94   0.352    -.3416103    .1229016
        213  |  -.0826282   .1443821    -0.57   0.569    -.3696985    .2044421
        214  |   -.115413   .1626024    -0.71   0.480    -.4387102    .2078843
        215  |   .8906456   .1168132     7.62   0.000     .6583897    1.122902
        216  |  -.0191493   .1559763    -0.12   0.903    -.3292719    .2909733
        217  |   .3620956   .3832488     0.94   0.347    -.3999057    1.124097
        219  |    .605579   .4183998     1.45   0.151    -.2263119     1.43747
        221  |   .4919483   .4756174     1.03   0.304    -.4537065    1.437603
        227  |  -.0171752   .1521262    -0.11   0.910    -.3196428    .2852924
        231  |  -.0033256   .1291254    -0.03   0.980    -.2600616    .2534103
        233  |   .0189663   .1150413     0.16   0.869    -.2097665    .2476992
        235  |   -.068243     .11871    -0.57   0.567    -.3042703    .1677843
        237  |    1.01322   .1419641     7.14   0.000      .730957    1.295482
        239  |   1.016263   .1361324     7.47   0.000     .7455951     1.28693
        241  |  -.0129868   .1161259    -0.11   0.911    -.2438762    .2179026
        243  |  -.4208334   .1593235    -2.64   0.010    -.7376112   -.1040555
        244  |  -.0923828   .1172052    -0.79   0.433    -.3254181    .1406526
        245  |  -.0129868   .1161259    -0.11   0.911    -.2438762    .2179026
        247  |   .4394585    .321608     1.37   0.175    -.1999843    1.078901
        248  |  -.0873913   .1207117    -0.72   0.471    -.3273985     .152616
        250  |   -.131134   .1513255    -0.87   0.389    -.4320096    .1697416
        251  |   .0287241   .2837714     0.10   0.920    -.5354896    .5929377
        252  |  -.0393965   .1056275    -0.37   0.710    -.2494122    .1706192
        267  |  -.1076681   .1114115    -0.97   0.337     -.329184    .1138477
        269  |  -.0129868   .1161259    -0.11   0.911    -.2438762    .2179026
        271  |  -.4030799   .2153336    -1.87   0.065    -.8312207     .025061
        272  |  -.1456431   .1132639    -1.29   0.202     -.370842    .0795558
        274  |   -.131134   .1513255    -0.87   0.389    -.4320096    .1697416
        275  |  -.2153725   .1436876    -1.50   0.138    -.5010619     .070317
        276  |  -.0923828   .1172052    -0.79   0.433    -.3254181    .1406526
        278  |   -.131134   .1513255    -0.87   0.389    -.4320096    .1697416
        279  |  -.1760207   .1157477    -1.52   0.132    -.4061582    .0541168
        280  |  -.0393965   .1056275    -0.37   0.710    -.2494122    .1706192
        283  |   -.079757   .0990217    -0.81   0.423    -.2766387    .1171247
        284  |   .8643399   .1246507     6.93   0.000     .6165008    1.112179
        285  |  -.1092504   .1337456    -0.82   0.416    -.3751725    .1566716
        287  |  -.1076681   .1114115    -0.97   0.337     -.329184    .1138477
             |
      fYes_T |   .0962637   .0573074     1.68   0.097    -.0176789    .2102062
        mage |  -.0001054    .004952    -0.02   0.983    -.0099512    .0097404
    mmarried |  -.0367012   .0816066    -0.45   0.654     -.198957    .1255546
       makan |  -.0960514   .0706391    -1.36   0.178    -.2365009    .0443982
mselfemplo~d |  -.0947441   .0606839    -1.56   0.122       -.2154    .0259119
       m2q1a |   .0021078   .0175274     0.12   0.905    -.0327413    .0369569
      2.m3q1 |  -.0434555   .0674693    -0.64   0.521    -.1776026    .0906916
        trt2 |  -.2856581   .1556667    -1.84   0.070    -.5951652    .0238491
        trt3 |  -.2777212   .1226243    -2.26   0.026    -.5215312   -.0339112
        trt4 |  -.3173809   .1234542    -2.57   0.012    -.5628409   -.0719208
    vfemaleE |  -.2114667   .1422095    -1.49   0.141    -.4942172    .0712839
             |
      c.trt2#|
  c.vfemaleE |   .2077343   .1860735     1.12   0.267    -.1622296    .5776983
             |
      c.trt3#|
  c.vfemaleE |   .1158635   .1662911     0.70   0.488    -.2147677    .4464947
             |
      c.trt4#|
  c.vfemaleE |    .229136   .1812457     1.26   0.210    -.1312291    .5895011
             |
       _cons |   .4230468   .1551659     2.73   0.008     .1145354    .7315582
------------------------------------------------------------------------------

. reg fdamt i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a i
> .m3q1 trt2 trt3 trt4 vfemale c.trt2#c.vfemale c.trt3#c.vfemale c.trt4#c.vfem
> ale, r cluster(ge02) level(95)

Linear regression                               Number of obs     =        318
                                                F(75, 85)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6952
                                                Root MSE          =     .98565

                                  (Std. err. adjusted for 86 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
       fdamt | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.6959923   .4493636    -1.55   0.125    -1.589448     .197463
          5  |   2.021957   1.573178     1.29   0.202    -1.105942    5.149856
          6  |   .1913045   .5993667     0.32   0.750    -1.000397    1.383006
          7  |  -.6959923   .4493636    -1.55   0.125    -1.589448     .197463
          8  |   .0218047   .2710036     0.08   0.936    -.5170231    .5606324
         11  |    .108516   .3718209     0.29   0.771    -.6307637    .8477956
         14  |    .108516   .3718209     0.29   0.771    -.6307637    .8477956
         18  |   .1913045   .5993667     0.32   0.750    -1.000397    1.383006
         22  |  -.6959923   .4493636    -1.55   0.125    -1.589448     .197463
         23  |  -.0022277   .3460316    -0.01   0.995    -.6902311    .6857758
         24  |   .1913045   .5993667     0.32   0.750    -1.000397    1.383006
         26  |  -.2370308   .4058633    -0.58   0.561    -1.043996    .5699341
         27  |   .1913045   .5993667     0.32   0.750    -1.000397    1.383006
         29  |  -.2690825   .4025178    -0.67   0.506    -1.069396    .5312308
         30  |   .1913045   .5993667     0.32   0.750    -1.000397    1.383006
         32  |   .0078306    .352268     0.02   0.982    -.6925727    .7082338
         33  |   .1913045   .5993667     0.32   0.750    -1.000397    1.383006
         34  |  -.6959923   .4493636    -1.55   0.125    -1.589448     .197463
         35  |  -.0872858   .2271913    -0.38   0.702     -.539003    .3644315
         37  |  -.2385284   .3344281    -0.71   0.478    -.9034611    .4264042
         38  |   .2435403    .721094     0.34   0.736    -1.190188    1.677268
         39  |  -.1701768   .3098602    -0.55   0.584    -.7862618    .4459082
         40  |   .9227197   .8212808     1.12   0.264    -.7102067    2.555646
         41  |  -.1636371   .3665031    -0.45   0.656    -.8923435    .5650693
         42  |  -.2049509   .4811099    -0.43   0.671    -1.161526    .7516245
         51  |  -.2327558   .3179449    -0.73   0.466    -.8649153    .3994038
         52  |  -.6852994   .5166205    -1.33   0.188    -1.712479    .3418805
         53  |   -.348869   .3484454    -1.00   0.320    -1.041672    .3439338
         54  |  -.1156837   .4575469    -0.25   0.801     -1.02541    .7940422
         55  |  -.1395403   .3762644    -0.37   0.712    -.8876547    .6085741
         56  |   .0071072   .3540398     0.02   0.984    -.6968188    .7110331
         57  |    1.41084   1.152003     1.22   0.224     -.879652    3.701331
         58  |   .4424482   .6903679     0.64   0.523    -.9301882    1.815085
         61  |  -.3990074    .333817    -1.20   0.235    -1.062725    .2647103
         62  |  -.2878715   .3655933    -0.79   0.433    -1.014769     .439026
         63  |  -.4845661   .6271857    -0.77   0.442    -1.731579     .762447
         64  |  -.3544163    .459669    -0.77   0.443    -1.268361    .5595288
         65  |  -.1902926    .507161    -0.38   0.708    -1.198665    .8180794
         66  |    3.62682   .4589149     7.90   0.000     2.714374    4.539266
         67  |   2.406431   1.138922     2.11   0.038     .1419484    4.670913
         68  |  -.3544163    .459669    -0.77   0.443    -1.268361    .5595288
         69  |   .2448997   .3562434     0.69   0.494    -.4634076    .9532071
         71  |   .0450802   .4057557     0.11   0.912    -.7616709    .8518313
         72  |  -.3544163    .459669    -0.77   0.443    -1.268361    .5595288
         89  |  -.0128717   .3872762    -0.03   0.974    -.7828804    .7571371
         90  |  -.1185406   .4272746    -0.28   0.782     -.968077    .7309958
         91  |  -.9082079   .5725473    -1.59   0.116    -2.046585    .2301695
         93  |   .0687148   .3222973     0.21   0.832    -.5720987    .7095283
         94  |  -.5573661   .4341899    -1.28   0.203    -1.420652    .3059197
         95  |  -.5646913   .4569666    -1.24   0.220    -1.473263    .3438807
         97  |   -.157113   .3451614    -0.46   0.650    -.8433863    .5291603
         98  |  -.2878715   .3655933    -0.79   0.433    -1.014769     .439026
         99  |  -.7913936   .6066909    -1.30   0.196    -1.997658    .4148705
        102  |   .1416758   .8613537     0.16   0.870    -1.570926    1.854278
        103  |   1.039945   .4302706     2.42   0.018      .184452    1.895439
        104  |    .277211   .5166931     0.54   0.593    -.7501133    1.304535
        107  |  -.1055131    .435778    -0.24   0.809    -.9719565    .7609304
        109  |   -.526273   .4976592    -1.06   0.293    -1.515753    .4632068
        110  |  -.2710448   .3464416    -0.78   0.436    -.9598635    .4177739
        113  |  -.2909427   .4650443    -0.63   0.533    -1.215575    .6336899
        114  |  -.6577674     .67074    -0.98   0.330    -1.991378    .6758434
        117  |  -.2909427   .4650443    -0.63   0.533    -1.215575    .6336899
        118  |   -.813494    .456126    -1.78   0.078    -1.720395    .0934067
        137  |  -.6593155   .4658359    -1.42   0.161    -1.585522    .2668912
        138  |   -.627354   .4360979    -1.44   0.154    -1.494433    .2397255
        141  |  -.7616033   .5032003    -1.51   0.134      -1.7621    .2388937
        142  |  -.9368191   .4709993    -1.99   0.050    -1.873292   -.0003463
        145  |  -.3932306   .4693761    -0.84   0.405    -1.326476    .5400149
        146  |  -.6470208   .4213244    -1.54   0.128    -1.484727     .190685
        149  |   .6579133   1.056828     0.62   0.535    -1.443344    2.759171
        150  |   .4300731   .5301256     0.81   0.419    -.6239587    1.484105
        153  |  -.2909427   .4650443    -0.63   0.533    -1.215575    .6336899
        154  |  -.1046525   .4266428    -0.25   0.807    -.9529326    .7436276
        157  |   -.992415   .6902923    -1.44   0.154    -2.364901     .380071
        158  |   .1232943   .4053021     0.30   0.762    -.6825548    .9291435
        160  |   4.000558   .4523178     8.84   0.000     3.101229    4.899887
        162  |   1.000558   .4523178     2.21   0.030     .1012287    1.899887
        171  |   -.992415   .6902923    -1.44   0.154    -2.364901     .380071
        172  |    .246031   .4170451     0.59   0.557    -.5831664    1.075228
        173  |   -.992415   .6902923    -1.44   0.154    -2.364901     .380071
        174  |   .0005577   .4523178     0.00   0.999    -.8987713    .8998866
        175  |   -.992415   .6902923    -1.44   0.154    -2.364901     .380071
        176  |   .0005577   .4523178     0.00   0.999    -.8987713    .8998866
        178  |   .6232943   .5432254     1.15   0.254    -.4567833    1.703372
        180  |   1.623294   1.448693     1.12   0.266    -1.257095    4.503683
        181  |  -.5582722   .4157087    -1.34   0.183    -1.384813    .2682681
        182  |   .0107019    .471713     0.02   0.982    -.9271899    .9485936
        183  |   3.318988   .4376619     7.58   0.000     2.448799    4.189177
        184  |   1.005533   1.671275     0.60   0.549    -2.317408    4.328475
        185  |  -.7766182   .4490069    -1.73   0.087    -1.669364    .1161279
        186  |   .0317209   .5851838     0.05   0.957    -1.131781    1.195223
        187  |  -.6810117   .4376619    -1.56   0.123    -1.551201    .1891774
        189  |  -.6810117   .4376619    -1.56   0.123    -1.551201    .1891774
        191  |  -.6810117   .4376619    -1.56   0.123    -1.551201    .1891774
        193  |  -.6810117   .4376619    -1.56   0.123    -1.551201    .1891774
        195  |   -.510469   .4278411    -1.19   0.236    -1.361132    .3401937
        196  |   2.444779   2.127438     1.15   0.254    -1.785138    6.674696
        197  |  -.1430611   .5869058    -0.24   0.808    -1.309987    1.023865
        198  |  -.5026348   .3867706    -1.30   0.197    -1.271638    .2663687
        199  |  -.4763944   .3827767    -1.24   0.217    -1.237457    .2846682
        200  |  -.1176323    .387864    -0.30   0.762    -.8888098    .6535452
        201  |   2.271185   .4046284     5.61   0.000     1.466676    3.075695
        202  |   4.865738   .3342187    14.56   0.000     4.201222    5.530254
        203  |   4.318988   .4376619     9.87   0.000     3.448799    5.189177
        204  |  -.0313363   .4614887    -0.07   0.946    -.9488994    .8862269
        205  |   -.182197   .3663671    -0.50   0.620    -.9106329    .5462389
        206  |    1.68036   1.811407     0.93   0.356    -1.921202    5.281922
        207  |   .8915829   1.443797     0.62   0.539    -1.979072    3.762238
        210  |  -.2771065   .3994227    -0.69   0.490    -1.071266    .5170527
        211  |  -.6454793   .4605581    -1.40   0.165    -1.561192    .2702335
        212  |  -.4469234   .3992419    -1.12   0.266    -1.240723    .3468765
        213  |  -.3340094   .5076062    -0.66   0.512    -1.343267    .6752478
        214  |  -.5607291   .6188345    -0.91   0.367    -1.791138    .6696798
        215  |   .5530766   .3992419     1.39   0.170    -.2407233    1.346876
        216  |  -.1923563   .5865294    -0.33   0.744    -1.358534    .9738212
        217  |   1.610885   1.824302     0.88   0.380    -2.016317    5.238087
        219  |   2.308553   1.751581     1.32   0.191    -1.174059    5.791165
        221  |   .3431376   .5148947     0.67   0.507     -.680611    1.366886
        227  |  -.3132798   .5100033    -0.61   0.541    -1.327303    .7007435
        231  |  -.1785723   .4756287    -0.38   0.708     -1.12425     .767105
        233  |  -.0320175   .3581624    -0.09   0.929    -.7441402    .6801053
        235  |  -.3997663   .3684725    -1.08   0.281    -1.132388    .3328557
        237  |   1.987842   .8761795     2.27   0.026     .2457625    3.729922
        239  |   5.906552   1.179018     5.01   0.000     3.562349    8.250755
        241  |  -.1843615   .4098888    -0.45   0.654    -.9993303    .6306072
        243  |   -1.23458    .592569    -2.08   0.040    -2.412765   -.0563935
        244  |  -.2219364   .3844417    -0.58   0.565    -.9863094    .5424366
        245  |  -.1843615   .4098888    -0.45   0.654    -.9993303    .6306072
        247  |   1.998083   1.348982     1.48   0.142    -.6840556    4.680221
        248  |  -.2455929   .3577118    -0.69   0.494    -.9568198     .465634
        250  |  -.3658459   .4881402    -0.75   0.456    -1.336399    .6047076
        251  |  -.6247836   .4687839    -1.33   0.186    -1.556852    .3072843
        252  |  -.1201809   .3331214    -0.36   0.719    -.7825155    .5421537
        267  |   -.408736   .3782972    -1.08   0.283    -1.160892    .3434202
        269  |  -.1843615   .4098888    -0.45   0.654    -.9993303    .6306072
        271  |  -1.232807   .6711469    -1.84   0.070    -2.567227    .1016125
        272  |  -.4412407   .3640298    -1.21   0.229    -1.165029    .2825481
        274  |  -.3658459   .4881402    -0.75   0.456    -1.336399    .6047076
        275  |  -.6674025   .4831995    -1.38   0.171    -1.628132    .2933275
        276  |  -.2219364   .3844417    -0.58   0.565    -.9863094    .5424366
        278  |  -.3658459   .4881402    -0.75   0.456    -1.336399    .6047076
        279  |  -.6338759   .4080887    -1.55   0.124    -1.445266    .1775139
        280  |  -.1201809   .3331214    -0.36   0.719    -.7825155    .5421537
        283  |  -.2655032   .3494756    -0.76   0.450    -.9603543     .429348
        284  |   .5114464   .3864775     1.32   0.189    -.2569744    1.279867
        285  |  -.5527342   .4593722    -1.20   0.232    -1.466089    .3606207
        287  |   -.408736   .3782972    -1.08   0.283    -1.160892    .3434202
             |
      fYes_T |   .3683727   .1901707     1.94   0.056    -.0097377    .7464831
        mage |   .0077356   .0170008     0.46   0.650    -.0260665    .0415377
    mmarried |  -.1218321   .2762464    -0.44   0.660     -.671084    .4274198
       makan |  -.2857687   .2243003    -1.27   0.206    -.7317376    .1602003
mselfemplo~d |  -.3132316   .2225312    -1.41   0.163    -.7556832    .1292199
       m2q1a |    .007245   .0483078     0.15   0.881    -.0888038    .1032938
      2.m3q1 |  -.0726847   .2177458    -0.33   0.739    -.5056216    .3602522
        trt2 |  -.8481034   .4654054    -1.82   0.072    -1.773454    .0772472
        trt3 |  -.8677168   .3860456    -2.25   0.027    -1.635279   -.1001547
        trt4 |  -.8403336   .3876934    -2.17   0.033    -1.611172   -.0694952
    vfemaleE |  -.6166202   .4701877    -1.31   0.193    -1.551479     .318239
             |
      c.trt2#|
  c.vfemaleE |   .6061324   .5675183     1.07   0.289    -.5222461    1.734511
             |
      c.trt3#|
  c.vfemaleE |   .4996949   .5086908     0.98   0.329    -.5117188    1.511109
             |
      c.trt4#|
  c.vfemaleE |    .513317   .5999074     0.86   0.395    -.6794597    1.706094
             |
       _cons |   1.072132   .5449293     1.97   0.052    -.0113334    2.155598
------------------------------------------------------------------------------

. 
. 
. 
. 
. ** (2) Heterogeneity: Illiteracy + Bundled stores
. use "$dta_loc_repl/00_Raw_anon/analyzed_EndlineAuditData.dta", clear

. drop _merge

. merge m:m text_ge02 using "$dta_loc_repl/01_intermediate/mkt_aiVendorBetter.
> dta"

    Result                      Number of obs
    -----------------------------------------
    Not matched                            70
        from master                        60  (_merge==1)
        from using                         10  (_merge==2)

    Matched                             2,259  (_merge==3)
    -----------------------------------------

. keep if _merge==3
(70 observations deleted)

. 
. gen bundle=(m3q1==2) if !missing(m3q1) //bundle shops
(759 missing values generated)

. gen bundlek=(ffaq13==1) if !missing(ffaq13)
(255 missing values generated)

. gen vIncorrects = (m_corrects==0) //illiterate vendors

. gen cIncorrects = (c_corrects==0) //illiterate vendors

. gen vIncorrectsxFemale= (m_corrects==0 & mfemale==1) //illiterate female ven
> dors

. gen busInexperience = (m2q1b<12) //inexperienced vendors

. 
. *note: doesnt matter sv_fAmt_T / fYes_T
. gen mkt_c_corrects2No=1-mkt_c_corrects2

. gen mkt_c_fracAnyEducNo=1-mkt_c_fracAnyEduc

. pwcorr mkt_c_fracprimandlesssEduc mkt_c_fracAnyEducNo mkt_c_corrects2No, sig
>  // positively corrected: incorrectness & less/no formal educ

             | mk~sEduc mkt_~cNo mkt_~2No
-------------+---------------------------
mkt_c_~sEduc |   1.0000 
             |
             |
mkt_c_frac~o |   0.5951   1.0000 
             |   0.0000
             |
mkt_c_corr~o |   0.0094   0.0324   1.0000 
             |   0.6553   0.1242
             |

. 
. gen sv_fAmt_T0 = sv_fAmt_T
(2,108 missing values generated)

. replace sv_fAmt_T0=0 if fYes_T==0
(507 real changes made)

. 
. ** Table C.13 --------------------------------------------------------------
> ----
. *CUSTOMERS - base Illiteracy effects
. reg fd i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a i.m3
> q1 trt  c.trt#c.mkt_c_fracAnyEducNo mkt_c_fracAnyEducNo, r cluster(ge02) lev
> el(95)

Linear regression                               Number of obs     =        332
                                                F(76, 89)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.7119
                                                Root MSE          =     .28316

                                  (Std. err. adjusted for 90 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
          fd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.0234797   .1267146    -0.19   0.853    -.2752588    .2282995
          5  |   .4464984    .324745     1.37   0.173    -.1987629     1.09176
          6  |   .0339401   .1696985     0.20   0.842    -.3032471    .3711274
          7  |  -.0234797   .1267146    -0.19   0.853    -.2752588    .2282995
          8  |   .0284018   .0825164     0.34   0.732    -.1355565    .1923602
         11  |   .0539271    .102096     0.53   0.599    -.1489355    .2567897
         14  |   .0539271    .102096     0.53   0.599    -.1489355    .2567897
         18  |   .0339401   .1696985     0.20   0.842    -.3032471    .3711274
         22  |  -.0234797   .1267146    -0.19   0.853    -.2752588    .2282995
         23  |   -.002112   .1004409    -0.02   0.983     -.201686     .197462
         24  |   .0339401   .1696985     0.20   0.842    -.3032471    .3711274
         26  |  -.0427089   .1401023    -0.30   0.761    -.3210891    .2356714
         27  |   .0339401   .1696985     0.20   0.842    -.3032471    .3711274
         29  |  -.1035735    .161638    -0.64   0.523    -.4247448    .2175978
         30  |   .0339401   .1696985     0.20   0.842    -.3032471    .3711274
         32  |   .1436092   .1776607     0.81   0.421    -.2093987    .4966172
         33  |   .0339401   .1696985     0.20   0.842    -.3032471    .3711274
         34  |  -.0234797   .1267146    -0.19   0.853    -.2752588    .2282995
         35  |   .0071197   .0795592     0.09   0.929    -.1509627     .165202
         37  |  -.0473655   .0984796    -0.48   0.632    -.2430423    .1483112
         38  |   .0529019   .1434984     0.37   0.713    -.2322264    .3380302
         39  |   -.030445   .1052667    -0.29   0.773    -.2396078    .1787178
         40  |   .2296387   .2103863     1.09   0.278    -.1883944    .6476718
         41  |   .0937213    .214343     0.44   0.663    -.3321737    .5196163
         42  |   .3254905   .2766157     1.18   0.242     -.224139    .8751199
         51  |  -.0743455   .0876403    -0.85   0.399     -.248485     .099794
         52  |  -.1589652   .1182669    -1.34   0.182    -.3939589    .0760285
         53  |  -.0394521    .108965    -0.36   0.718    -.2559632     .177059
         54  |   .0326052   .1724603     0.19   0.850    -.3100697    .3752802
         55  |   -.020402   .1155559    -0.18   0.860    -.2500092    .2092052
         56  |   .0673436   .1462853     0.46   0.646    -.2233221    .3580094
         57  |   .8371603   .1112298     7.53   0.000     .6161491    1.058171
         58  |   .8949477   .1257243     7.12   0.000     .6451362    1.144759
         61  |  -.0429737   .1130506    -0.38   0.705    -.2676028    .1816555
         62  |  -.0973245   .1028778    -0.95   0.347    -.3017404    .1070914
         63  |   -.089158   .1579909    -0.56   0.574    -.4030826    .2247666
         64  |  -.0939782    .121683    -0.77   0.442    -.3357598    .1478033
         65  |   .0341882   .1638404     0.21   0.835    -.2913591    .3597356
         66  |   .9364344   .1262568     7.42   0.000     .6855649    1.187304
         67  |    .493696   .2278578     2.17   0.033     .0409474    .9464447
         68  |  -.0939782    .121683    -0.77   0.442    -.3357598    .1478033
         69  |   .1673429   .1259284     1.33   0.187    -.0828742      .41756
         71  |   .3581938   .2461195     1.46   0.149    -.1308404    .8472281
         72  |  -.0939782    .121683    -0.77   0.442    -.3357598    .1478033
         89  |   .0846091   .1379005     0.61   0.541    -.1893961    .3586144
         90  |   -.053853   .1210916    -0.44   0.658    -.2944593    .1867533
         91  |  -.1162215   .1716238    -0.68   0.500    -.4572343    .2247913
         93  |   .1142826   .1074488     1.06   0.290    -.0992158     .327781
         94  |  -.1156732   .1490077    -0.78   0.440    -.4117484    .1804019
         95  |    -.17826   .1716463    -1.04   0.302    -.5193174    .1627975
         97  |   .0216165   .1133663     0.19   0.849      -.20364    .2468729
         98  |  -.0973245   .1028778    -0.95   0.347    -.3017404    .1070914
         99  |  -.1251429    .181697    -0.69   0.493     -.486171    .2358851
        102  |   .4450706   .5201008     0.86   0.394    -.5883586      1.4785
        103  |   1.120847   .0979777    11.44   0.000     .9261671    1.315526
        104  |   .8018064   .1433292     5.59   0.000     .5170144    1.086598
        107  |   .0678639   .1032748     0.66   0.513     -.137341    .2730688
        109  |  -.1547248   .1085699    -1.43   0.158    -.3704509    .0610012
        110  |  -.0248538   .1165423    -0.21   0.832    -.2564209    .2067132
        113  |  -.0940533   .0836554    -1.12   0.264    -.2602748    .0721683
        114  |  -.0668338   .1387428    -0.48   0.631    -.3425129    .2088452
        117  |  -.0940533   .0836554    -1.12   0.264    -.2602748    .0721683
        118  |  -.1076673   .1176455    -0.92   0.363    -.3414264    .1260918
        137  |  -.1982686   .0991171    -2.00   0.049    -.3952122    -.001325
        138  |   -.058798   .1135937    -0.52   0.606    -.2845063    .1669104
        141  |  -.2153964   .1100465    -1.96   0.053    -.4340565    .0032637
        142  |   -.014086   .1275392    -0.11   0.912    -.2675037    .2393318
        145  |  -.1111811   .0906407    -1.23   0.223    -.2912822      .06892
        146  |  -.0631879    .120025    -0.53   0.600     -.301675    .1752992
        149  |   .3973828   .4922046     0.81   0.422    -.5806173    1.375383
        150  |    .924093   .1106018     8.36   0.000     .7043297    1.143856
        153  |  -.0940533   .0836554    -1.12   0.264    -.2602748    .0721683
        154  |   .0282471   .1065902     0.27   0.792    -.1835453    .2400395
        157  |  -.0894685    .164917    -0.54   0.589     -.417155     .238218
        158  |  -.0356892   .2782638    -0.13   0.898    -.5885934     .517215
        159  |   .0387014   .0951945     0.41   0.685     -.150448    .2278508
        160  |   .7302812   .1937395     3.77   0.000     .3453248    1.115237
        162  |   .7302812   .1937395     3.77   0.000     .3453248    1.115237
        171  |  -.2176384   .1488659    -1.46   0.147    -.5134317    .0781548
        172  |   .1983404   .1526504     1.30   0.197    -.1049727    .5016536
        173  |  -.2176384   .1488659    -1.46   0.147    -.5134317    .0781548
        174  |  -.2697188   .1937395    -1.39   0.167    -.6546752    .1152375
        175  |  -.0894685    .164917    -0.54   0.589     -.417155     .238218
        176  |  -.2697188   .1937395    -1.39   0.167    -.6546752    .1152375
        177  |   .9344861   .1144795     8.16   0.000     .7070178    1.161954
        178  |   .4643108   .3049102     1.52   0.131    -.1415392    1.070161
        180  |   .4643108   .3049102     1.52   0.131    -.1415392    1.070161
        181  |   -.142024   .1164467    -1.22   0.226    -.3734011    .0893531
        182  |  -.0346072   .1061965    -0.33   0.745    -.2456173    .1764029
        183  |   .5438665   .2249614     2.42   0.018     .0968731      .99086
        184  |   .2548965   .4041181     0.63   0.530    -.5480775     1.05787
        185  |  -.2029692   .1420668    -1.43   0.157    -.4852528    .0793145
        186  |  -.0238388   .1206337    -0.20   0.844    -.2635355    .2158578
        187  |  -.4561335   .2249614    -2.03   0.046    -.9031269     -.00914
        189  |  -.4561335   .2249614    -2.03   0.046    -.9031269     -.00914
        191  |  -.4561335   .2249614    -2.03   0.046    -.9031269     -.00914
        193  |  -.4561335   .2249614    -2.03   0.046    -.9031269     -.00914
        195  |  -.2686062   .2032458    -1.32   0.190    -.6724511    .1352388
        196  |   .4422055   .4255122     1.04   0.302    -.4032781    1.287689
        197  |   .1213446   .4735532     0.26   0.798    -.8195955    1.062285
        198  |  -.1367818   .1047222    -1.31   0.195    -.3448627     .071299
        199  |  -.2119887   .1673567    -1.27   0.209     -.544523    .1205455
        200  |  -.0601509   .1070125    -0.56   0.575    -.2727825    .1524808
        201  |   .6704487    .171932     3.90   0.000     .3288235    1.012074
        202  |   .9674335   .0844801    11.45   0.000     .7995733    1.135294
        203  |   .5438665   .2249614     2.42   0.018     .0968731      .99086
        204  |   -.056144   .1082599    -0.52   0.605    -.2712541    .1589661
        205  |  -.0136352   .0973744    -0.14   0.889     -.207116    .1798457
        206  |   .4810244   .4237621     1.14   0.259    -.3609818    1.323031
        207  |    .198936   .2666876     0.75   0.458    -.3309666    .7288386
        210  |  -.0500448   .1014758    -0.49   0.623    -.2516752    .1515855
        211  |  -.1195217   .1099362    -1.09   0.280    -.3379626    .0989191
        212  |  -.0964888   .0965547    -1.00   0.320     -.288341    .0953633
        213  |  -.0678462   .1327144    -0.51   0.610     -.331547    .1958545
        214  |  -.1222074   .1404308    -0.87   0.387    -.4012405    .1568257
        215  |   .9035112   .0965547     9.36   0.000      .711659    1.095363
        216  |   .4820079   .4229897     1.14   0.258    -.3584636    1.322479
        217  |   .3042494   .3666311     0.83   0.409    -.4242388    1.032738
        219  |    .376539   .3986208     0.94   0.347    -.4155121     1.16859
        221  |   .6076689   .3803755     1.60   0.114    -.1481292    1.363467
        227  |  -.0836195    .126436    -0.66   0.510    -.3348452    .1676061
        231  |  -.0638224    .121195    -0.53   0.600    -.3046344    .1769896
        233  |  -.0079807   .1037537    -0.08   0.939    -.2141372    .1981757
        235  |  -.0783739   .1068116    -0.73   0.465    -.2906062    .1338584
        237  |   1.008791   .1235425     8.17   0.000     .7633143    1.254267
        239  |   1.000143   .1066203     9.38   0.000     .7882908    1.211995
        241  |  -.0305594   .0890839    -0.34   0.732    -.2075673    .1464485
        243  |  -.2504995   .1061196    -2.36   0.020     -.461357    -.039642
        244  |  -.0771257   .0824016    -0.94   0.352     -.240856    .0866045
        245  |  -.0305594   .0890839    -0.34   0.732    -.2075673    .1464485
        247  |   .5638955    .306876     1.84   0.069    -.0458605    1.173652
        248  |  -.0718938   .0842297    -0.85   0.396    -.2392564    .0954688
        250  |  -.1051598   .1173077    -0.90   0.372    -.3382478    .1279281
        251  |   .1070239   .3142065     0.34   0.734    -.5172977    .7313455
        252  |  -.0412776   .0859304    -0.48   0.632    -.2120196    .1294644
        267  |  -.1069527    .090175    -1.19   0.239    -.2861285    .0722232
        269  |  -.0305594   .0890839    -0.34   0.732    -.2075673    .1464485
        271  |  -.2859878   .1598416    -1.79   0.077    -.6035895     .031614
        272  |  -.1559568   .1060161    -1.47   0.145    -.3666085     .054695
        274  |  -.1051598   .1173077    -0.90   0.372    -.3382478    .1279281
        275  |    -.12643   .1534637    -0.82   0.412    -.4313591    .1784992
        276  |  -.0771257   .0824016    -0.94   0.352     -.240856    .0866045
        278  |  -.1051598   .1173077    -0.90   0.372    -.3382478    .1279281
        279  |  -.1257797   .1491136    -0.84   0.401    -.4220652    .1705059
        280  |  -.0412776   .0859304    -0.48   0.632    -.2120196    .1294644
        283  |  -.0215643   .1286149    -0.17   0.867    -.2771195    .2339908
        284  |   .8545071   .1084984     7.88   0.000     .6389231    1.070091
        285  |  -.1347747    .110499    -1.22   0.226     -.354334    .0847845
        287  |  -.1069527    .090175    -1.19   0.239    -.2861285    .0722232
             |
      fYes_T |   .1042153   .0546314     1.91   0.060     -.004336    .2127667
        mage |  -.0042284   .0030763    -1.37   0.173    -.0103409    .0018841
    mmarried |   .0537927   .0640365     0.84   0.403    -.0734464    .1810318
       makan |  -.0532442   .0617496    -0.86   0.391    -.1759394    .0694509
mselfemplo~d |  -.1160087   .0491812    -2.36   0.021    -.2137307   -.0182867
       m2q1a |   .0064497    .017672     0.36   0.716    -.0286642    .0415636
      2.m3q1 |  -.0947362    .069343    -1.37   0.175    -.2325192    .0430469
         trt |  -.0444982   .0904583    -0.49   0.624    -.2242369    .1352405
             |
       c.trt#|
          c. |
mkt_c_frac~o |  -1.139014   .5395969    -2.11   0.038    -2.211182   -.0668467
             |
mkt_c_frac~o |   1.063506   .5246913     2.03   0.046      .020956    2.106057
       _cons |   .2273477   .1287945     1.77   0.081    -.0285642    .4832595
------------------------------------------------------------------------------

. reg fdamt i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a i
> .m3q1 trt c.trt#c.mkt_c_fracAnyEducNo mkt_c_fracAnyEducNo, r cluster(ge02) l
> evel(95)

Linear regression                               Number of obs     =        332
                                                F(76, 89)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6880
                                                Root MSE          =     .95866

                                  (Std. err. adjusted for 90 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
       fdamt | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |   .0139577   .3897257     0.04   0.972     -.760419    .7883345
          5  |   2.001657   1.383432     1.45   0.151    -.7471933    4.750506
          6  |  -.0827543   .5309907    -0.16   0.877    -1.137821    .9723128
          7  |   .0139577   .3897257     0.04   0.972     -.760419    .7883345
          8  |   .0148427   .2745784     0.05   0.957    -.5307387     .560424
         11  |   .0846098   .3433263     0.25   0.806    -.5975722    .7667919
         14  |   .0846098   .3433263     0.25   0.806    -.5975722    .7667919
         18  |  -.0827543   .5309907    -0.16   0.877    -1.137821    .9723128
         22  |   .0139577   .3897257     0.04   0.972     -.760419    .7883345
         23  |  -.0856643   .3336318    -0.26   0.798    -.7485835    .5772549
         24  |  -.0827543   .5309907    -0.16   0.877    -1.137821    .9723128
         26  |  -.1964412   .4039022    -0.49   0.628    -.9989862    .6061038
         27  |  -.0827543   .5309907    -0.16   0.877    -1.137821    .9723128
         29  |  -.3693773   .4475417    -0.83   0.411    -1.258633    .5198786
         30  |  -.0827543   .5309907    -0.16   0.877    -1.137821    .9723128
         32  |   .0494544   .3114113     0.16   0.874    -.5693133     .668222
         33  |  -.0827543   .5309907    -0.16   0.877    -1.137821    .9723128
         34  |   .0139577   .3897257     0.04   0.972     -.760419    .7883345
         35  |  -.0808518   .2717561    -0.30   0.767    -.6208255    .4591219
         37  |  -.2282567   .3435698    -0.66   0.508    -.9109226    .4544091
         38  |   .3788014   .6669547     0.57   0.571    -.9464234    1.704026
         39  |  -.1627282   .3504337    -0.46   0.644    -.8590325    .5335761
         40  |   .9176541   .8507636     1.08   0.284    -.7727951    2.608103
         41  |  -.1545336   .3787613    -0.41   0.684    -.9071242    .5980569
         42  |  -.1420177    .630805    -0.23   0.822    -1.395414    1.111378
         51  |  -.2904456      .3036    -0.96   0.341    -.8936924    .3128012
         52  |  -.5660263   .3867958    -1.46   0.147    -1.334581    .2025288
         53  |  -.2646174   .3681161    -0.72   0.474    -.9960563    .4668215
         54  |  -.0170044   .5838532    -0.03   0.977    -1.177108      1.1431
         55  |    -.11382   .3810956    -0.30   0.766    -.8710488    .6434088
         56  |    .119689   .4629413     0.26   0.797    -.8001656    1.039544
         57  |   1.340412   1.083176     1.24   0.219    -.8118354     3.49266
         58  |   .6229435    .387312     1.61   0.111    -.1466372    1.392524
         61  |  -.2141685   .3858846    -0.56   0.580     -.980913     .552576
         62  |  -.3214959   .3950783    -0.81   0.418    -1.106508    .4635163
         63  |  -.4019889   .4859956    -0.83   0.410    -1.367652    .5636741
         64  |  -.2667511   .3876792    -0.69   0.493    -1.037061    .5035592
         65  |   .0467217   .5631395     0.08   0.934    -1.072225    1.165668
         66  |   3.842282   .3937503     9.76   0.000     3.059908    4.624655
         67  |   1.990278   .9809186     2.03   0.045      .041214    3.939343
         68  |  -.2667511   .3876792    -0.69   0.493    -1.037061    .5035592
         69  |   .4541685   .4117791     1.10   0.273    -.3640279    1.272365
         71  |   .1961265    .362359     0.54   0.590    -.5238732    .9161262
         72  |  -.2667511   .3876792    -0.69   0.493    -1.037061    .5035592
         89  |   .2408461   .4547459     0.53   0.598    -.6627243    1.144416
         90  |  -.0824363   .3698578    -0.22   0.824    -.8173359    .6524633
         91  |  -.4491853   .5276417    -0.85   0.397    -1.497598    .5992275
         93  |   .3076014   .3635858     0.85   0.400    -.4148358    1.030039
         94  |  -.3627583   .4481065    -0.81   0.420    -1.253136    .5276198
         95  |  -.6879145   .5470062    -1.26   0.212    -1.774804    .3989751
         97  |  -.0203648    .365569    -0.06   0.956    -.7467427    .7060131
         98  |  -.3214959   .3950783    -0.81   0.418    -1.106508    .4635163
         99  |  -.4975346   .5571291    -0.89   0.374    -1.604538    .6094691
        102  |   .3082621   .6251228     0.49   0.623    -.9338437    1.550368
        103  |   1.298222   .3181326     4.08   0.000     .6660995    1.930345
        104  |   .3231689   .4586412     0.70   0.483    -.5881414    1.234479
        107  |   .0783703   .3683558     0.21   0.832    -.6535449    .8102855
        109  |  -.5335049   .3996369    -1.33   0.185    -1.327575    .2605652
        110  |  -.1659286    .389064    -0.43   0.671    -.9389905    .6071332
        113  |  -.2919424   .2999152    -0.97   0.333    -.8878675    .3039828
        114  |  -.1998066   .4049701    -0.49   0.623    -1.004474    .6048603
        117  |  -.2919424   .2999152    -0.97   0.333    -.8878675    .3039828
        118  |    -.35359   .4104884    -0.86   0.391    -1.169222    .4620418
        137  |  -.7020223   .3519235    -1.99   0.049    -1.401287   -.0027578
        138  |  -.2116532   .4039032    -0.52   0.602      -1.0142    .5908938
        141  |  -.7750675   .3791583    -2.04   0.044    -1.528447    -.021688
        142  |   .1213202    .424273     0.29   0.776    -.7217013    .9643416
        145  |  -.3649875   .3109837    -1.17   0.244    -.9829055    .2529304
        146  |  -.1948255   .4086969    -0.48   0.635    -1.006898    .6172466
        149  |    .671535   .9969659     0.67   0.502    -1.309415    2.652485
        150  |   .7694815   .3456833     2.23   0.029     .0826162    1.456347
        153  |  -.2919424   .2999152    -0.97   0.333    -.8878675    .3039828
        154  |   .0330234   .3728577     0.09   0.930    -.7078369    .7738836
        157  |  -.2691794   .5324041    -0.51   0.614    -1.327055    .7886963
        158  |  -.2433693   .7809253    -0.31   0.756    -1.795051    1.308313
        159  |   .1089441   .3374864     0.32   0.748    -.5616342    .7795223
        160  |   3.133756   .5607657     5.59   0.000     2.019526    4.247985
        162  |   .1337556   .5607657     0.24   0.812    -.9804739    1.247985
        171  |  -.6473028   .5339601    -1.21   0.229     -1.70827    .4136645
        172  |   .3795059   .4919635     0.77   0.443    -.5980151    1.357027
        173  |  -.6473028   .5339601    -1.21   0.229     -1.70827    .4136645
        174  |  -.8662444   .5607657    -1.54   0.126    -1.980474    .2479852
        175  |  -.2691794   .5324041    -0.51   0.614    -1.327055    .7886963
        176  |  -.8662444   .5607657    -1.54   0.126    -1.980474    .2479852
        177  |   .6988641   .3993463     1.75   0.084    -.0946285    1.492357
        178  |   .2566307   .4970203     0.52   0.607     -.730938    1.244199
        180  |   1.256631    .990419     1.27   0.208    -.7113106    3.224572
        181  |  -.5188683   .3803598    -1.36   0.176    -1.274635    .2368985
        182  |  -.2177446   .3791126    -0.57   0.567    -.9710333    .5355441
        183  |    2.57996   .7040884     3.66   0.000     1.180951    3.978968
        184  |   .9467794   1.565539     0.60   0.547    -2.163913    4.057472
        185  |  -.8304881   .4727104    -1.76   0.082    -1.769754    .1087775
        186  |  -.1973146    .454559    -0.43   0.665    -1.100514    .7058845
        187  |   -1.42004   .7040884    -2.02   0.047    -2.819049   -.0210317
        189  |   -1.42004   .7040884    -2.02   0.047    -2.819049   -.0210317
        191  |   -1.42004   .7040884    -2.02   0.047    -2.819049   -.0210317
        193  |   -1.42004   .7040884    -2.02   0.047    -2.819049   -.0210317
        195  |  -.8136445   .6862861    -1.19   0.239     -2.17728    .5499914
        196  |   2.222742   2.205395     1.01   0.316    -2.159331    6.604815
        197  |  -.3492323   .7431722    -0.47   0.640      -1.8259    1.127435
        198  |  -.5658882   .3573107    -1.58   0.117    -1.275857    .1440805
        199  |  -.6825657   .5482979    -1.24   0.216    -1.772022    .4068906
        200  |  -.3047805   .3713877    -0.82   0.414     -1.04272    .4331589
        201  |   1.874736   .5426746     3.45   0.001     .7964528    2.953019
        202  |   4.844192     .30655    15.80   0.000     4.235083      5.4533
        203  |    3.57996   .7040884     5.08   0.000     2.180951    4.978968
        204  |  -.2586047   .3637234    -0.71   0.479    -.9813154     .464106
        205  |  -.0371077   .3340442    -0.11   0.912    -.7008464    .6266309
        206  |   1.909636   1.737362     1.10   0.275    -1.542465    5.361737
        207  |   1.006781   1.373936     0.73   0.466    -1.723201    3.736764
        210  |  -.1514262   .3636991    -0.42   0.678    -.8740885    .5712361
        211  |  -.4248129   .3960652    -1.07   0.286    -1.211786    .3621602
        212  |  -.3270263   .3185756    -1.03   0.307    -.9600292    .3059765
        213  |  -.2670379   .4636893    -0.58   0.566    -1.188379     .654303
        214  |  -.5018127   .4685346    -1.07   0.287    -1.432781    .4291556
        215  |   .6729737   .3185756     2.11   0.037     .0399708    1.305977
        216  |   1.408267   1.246918     1.13   0.262    -1.069332    3.885867
        217  |   1.437005   1.833505     0.78   0.435     -2.20613     5.08014
        219  |   1.476216   1.549734     0.95   0.343    -1.603071    4.555504
        221  |   .4262538   .5231952     0.81   0.417     -.613324    1.465831
        227  |  -.4245292   .4387363    -0.97   0.336    -1.296289    .4472305
        231  |  -.3663549   .4486902    -0.82   0.416    -1.257893     .525183
        233  |  -.1391288   .3644584    -0.38   0.704       -.8633    .5850423
        235  |  -.4041707   .3674862    -1.10   0.274    -1.134358    .3260165
        237  |   1.927639   .9904092     1.95   0.055     -.040283    3.895561
        239  |    5.91631   1.115684     5.30   0.000      3.69947     8.13315
        241  |  -.1584745   .3161489    -0.50   0.617    -.7866556    .4697066
        243  |  -.7220359   .3739295    -1.93   0.057    -1.465026    .0209541
        244  |   -.272643   .2941591    -0.93   0.357    -.8571309     .311845
        245  |  -.1584745   .3161489    -0.50   0.617    -.7866556    .4697066
        247  |   2.378499    1.33833     1.78   0.079    -.2807336    5.037731
        248  |  -.2604336   .2944413    -0.88   0.379    -.8454821    .3246149
        250  |  -.3729865   .3623699    -1.03   0.306    -1.093008    .3470348
        251  |  -.3767102   .3888184    -0.97   0.335    -1.149284    .3958637
        252  |  -.2018398   .3136758    -0.64   0.522     -.825107    .4214273
        267  |   -.351445   .3091322    -1.14   0.259    -.9656841    .2627941
        269  |  -.1584745   .3161489    -0.50   0.617    -.7866556    .4697066
        271  |  -.8893428   .5072382    -1.75   0.083    -1.897214    .1185286
        272  |  -.6363385   .3671417    -1.73   0.087    -1.365841    .0931642
        274  |  -.3729865   .3623699    -1.03   0.306    -1.093008    .3470348
        275  |  -.3997059   .4801765    -0.83   0.407    -1.353806    .5543945
        276  |   -.272643   .2941591    -0.93   0.357    -.8571309     .311845
        278  |  -.3729865   .3623699    -1.03   0.306    -1.093008    .3470348
        279  |  -.5097639   .4716102    -1.08   0.283    -1.446843    .4273156
        280  |  -.2018398   .3136758    -0.64   0.522     -.825107    .4214273
        283  |  -.0996839   .4082861    -0.24   0.808    -.9109398    .7115719
        284  |   .3880802   .3718794     1.04   0.300    -.3508362    1.126997
        285  |  -.5685545   .3726298    -1.53   0.131    -1.308962    .1718531
        287  |   -.351445   .3091322    -1.14   0.259    -.9656841    .2627941
             |
      fYes_T |     .41008   .1789217     2.29   0.024     .0545664    .7655935
        mage |  -.0135425   .0103889    -1.30   0.196     -.034185    .0070999
    mmarried |   .2486856   .2317062     1.07   0.286    -.2117096    .7090808
       makan |  -.1279242   .1940441    -0.66   0.511    -.5134858    .2576373
mselfemplo~d |  -.4429503   .2069169    -2.14   0.035    -.8540899   -.0318108
       m2q1a |   .0297513   .0489926     0.61   0.545     -.067596    .1270987
      2.m3q1 |   -.248628   .2320811    -1.07   0.287    -.7097682    .2125122
         trt |  -.0567728   .2690021    -0.21   0.833    -.5912743    .4777286
             |
       c.trt#|
          c. |
mkt_c_frac~o |  -3.443869   1.635806    -2.11   0.038    -6.694181   -.1935563
             |
mkt_c_frac~o |   3.546546   1.730049     2.05   0.043      .108976    6.984116
       _cons |   .6331079    .429019     1.48   0.144    -.2193437    1.485559
------------------------------------------------------------------------------

. 
. reg fd i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a i.m3
> q1 trt2  c.trt2#c.mkt_c_fracAnyEducNo trt3 c.trt3#c.mkt_c_fracAnyEducNo trt4
>  c.trt4#c.mkt_c_fracAnyEducNo mkt_c_fracAnyEducNo, r cluster(ge02) level(95)

Linear regression                               Number of obs     =        332
                                                F(80, 89)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.7137
                                                Root MSE          =     .28559

                                  (Std. err. adjusted for 90 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
          fd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |    -.00385   .1350746    -0.03   0.977    -.2722404    .2645405
          5  |   .4571579   .3298259     1.39   0.169    -.1981992    1.112515
          6  |   .0346067   .1811969     0.19   0.849    -.3254277     .394641
          7  |    -.00385   .1350746    -0.03   0.977    -.2722404    .2645405
          8  |   .0626411   .1027158     0.61   0.544    -.1414531    .2667353
         11  |   .0655496   .1147078     0.57   0.569    -.1623723    .2934715
         14  |   .0655496   .1147078     0.57   0.569    -.1623723    .2934715
         18  |   .0346067   .1811969     0.19   0.849    -.3254277     .394641
         22  |    -.00385   .1350746    -0.03   0.977    -.2722404    .2645405
         23  |    .050927   .1324618     0.38   0.702    -.2122717    .3141257
         24  |   .0346067   .1811969     0.19   0.849    -.3254277     .394641
         26  |   -.020463   .1569949    -0.13   0.897    -.3324085    .2914825
         27  |   .0346067   .1811969     0.19   0.849    -.3254277     .394641
         29  |  -.0885864   .1758622    -0.50   0.616    -.4380209    .2608482
         30  |   .0346067   .1811969     0.19   0.849    -.3254277     .394641
         32  |   .1638125   .1852313     0.88   0.379    -.2042382    .5318631
         33  |   .0346067   .1811969     0.19   0.849    -.3254277     .394641
         34  |    -.00385   .1350746    -0.03   0.977    -.2722404    .2645405
         35  |   .0146268   .0898913     0.16   0.871    -.1639853    .1932389
         37  |  -.0342287   .1157113    -0.30   0.768    -.2641446    .1956872
         38  |   .0758973   .1569896     0.48   0.630    -.2360378    .3878324
         39  |  -.0278333   .1237314    -0.22   0.823    -.2736849    .2180183
         40  |   .2633063   .2188883     1.20   0.232    -.1716201    .6982327
         41  |   .1030359   .2299202     0.45   0.655    -.3538107    .5598826
         42  |   .3487055   .2786481     1.25   0.214    -.2049624    .9023733
         51  |  -.0625949   .1012754    -0.62   0.538     -.263827    .1386372
         52  |  -.1385529   .1363918    -1.02   0.312    -.4095606    .1324548
         53  |  -.0510903   .1293383    -0.40   0.694    -.3080827    .2059022
         54  |   .0779619   .1821378     0.43   0.670     -.283942    .4398658
         55  |  -.0048825   .1304084    -0.04   0.970    -.2640012    .2542362
         56  |   .1128984   .1562319     0.72   0.472     -.197531    .4233279
         57  |   .8467104   .1236316     6.85   0.000     .6010571    1.092364
         58  |   .9185941   .1401456     6.55   0.000     .6401277    1.197061
         61  |   -.026493   .1244987    -0.21   0.832    -.2738692    .2208833
         62  |  -.0854151   .1151054    -0.74   0.460     -.314127    .1432968
         63  |  -.0933947    .173319    -0.54   0.591    -.4377758    .2509864
         64  |  -.0747106   .1337164    -0.56   0.578    -.3404022     .190981
         65  |   .0563722   .1719067     0.33   0.744    -.2852027    .3979471
         66  |   .9545002   .1339762     7.12   0.000     .6882923    1.220708
         67  |   .5078625   .2358474     2.15   0.034     .0392388    .9764862
         68  |  -.0747106   .1337164    -0.56   0.578    -.3404022     .190981
         69  |    .195163   .1325007     1.47   0.144    -.0681131    .4584391
         71  |   .3598515   .2610604     1.38   0.172    -.1588699     .878573
         72  |  -.0747106   .1337164    -0.56   0.578    -.3404022     .190981
         89  |   .1059148   .1452209     0.73   0.468    -.1826361    .3944656
         90  |  -.0369314   .1292146    -0.29   0.776    -.2936781    .2198153
         91  |  -.1074226   .1738827    -0.62   0.538    -.4529238    .2380786
         93  |   .1364151   .1172274     1.16   0.248    -.0965133    .3693436
         94  |  -.0979046   .1584465    -0.62   0.538    -.4127345    .2169253
         95  |  -.1812801   .1964065    -0.92   0.359    -.5715356    .2089755
         97  |   .0450613   .1263919     0.36   0.722    -.2060767    .2961992
         98  |  -.0854151   .1151054    -0.74   0.460     -.314127    .1432968
         99  |  -.1083831   .2009858    -0.54   0.591    -.5077376    .2909714
        102  |   .4584213   .5224672     0.88   0.383    -.5797099    1.496553
        103  |   1.115689   .1224263     9.11   0.000     .8724306    1.358947
        104  |   .8204798   .1551776     5.29   0.000     .5121452    1.128814
        107  |   .0590438    .137168     0.43   0.668     -.213506    .3315936
        109  |  -.1381478    .140949    -0.98   0.330    -.4182105    .1419149
        110  |   .0195665   .1296667     0.15   0.880    -.2380785    .2772116
        113  |  -.0466207   .1178611    -0.40   0.693    -.2808082    .1875667
        114  |  -.0620577   .1405351    -0.44   0.660    -.3412981    .2171826
        117  |  -.0466207   .1178611    -0.40   0.693    -.2808082    .1875667
        118  |  -.0623677   .1390907    -0.45   0.655    -.3387379    .2140025
        137  |  -.1514304   .1317692    -1.15   0.254     -.413253    .1103923
        138  |  -.0442725   .1226347    -0.36   0.719    -.2879449       .1994
        141  |  -.2296749   .1306202    -1.76   0.082    -.4892144    .0298647
        142  |   .0047545   .1386546     0.03   0.973    -.2707493    .2802584
        145  |  -.1248652   .1144681    -1.09   0.278    -.3523109    .1025804
        146  |  -.0293648   .1291966    -0.23   0.821    -.2860757     .227346
        149  |    .414257   .4712152     0.88   0.382    -.5220375    1.350552
        150  |   .9430879   .1189309     7.93   0.000     .7067748    1.179401
        153  |  -.0466207   .1178611    -0.40   0.693    -.2808082    .1875667
        154  |   .0224925   .1185254     0.19   0.850     -.213015        .258
        157  |  -.1049591   .1628282    -0.64   0.521    -.4284954    .2185772
        158  |   -.067003   .2783286    -0.24   0.810    -.6200361      .48603
        159  |  -.0008127   .1353248    -0.01   0.995    -.2697003    .2680749
        160  |   .7340937   .2051437     3.58   0.001     .3264776     1.14171
        162  |   .7340937   .2051437     3.58   0.001     .3264776     1.14171
        171  |  -.2091055   .1524519    -1.37   0.174    -.5120241    .0938131
        172  |   .1319002   .2315475     0.57   0.570    -.3281798    .5919802
        173  |  -.2091055   .1524519    -1.37   0.174    -.5120241    .0938131
        174  |  -.2659063   .2051437    -1.30   0.198    -.6735224    .1417098
        175  |  -.1049591   .1628282    -0.64   0.521    -.4284954    .2185772
        176  |  -.2659063   .2051437    -1.30   0.198    -.6735224    .1417098
        177  |   .8943777   .1546699     5.78   0.000     .5870519    1.201703
        178  |    .432997   .3582859     1.21   0.230    -.2789094    1.144903
        180  |    .432997   .3582859     1.21   0.230    -.2789094    1.144903
        181  |  -.1492117   .1336084    -1.12   0.267    -.4146886    .1162653
        182  |   .0011607   .1236488     0.01   0.993    -.2445269    .2468483
        183  |   .5381803   .2314486     2.33   0.022     .0782969    .9980638
        184  |   .2705708    .425048     0.64   0.526    -.5739906    1.115132
        185  |  -.2362215   .1820366    -1.30   0.198    -.5979243    .1254812
        186  |   .0253154   .1451292     0.17   0.862    -.2630533    .3136841
        187  |  -.4618197   .2314486    -2.00   0.049    -.9217031   -.0019362
        189  |  -.4618197   .2314486    -2.00   0.049    -.9217031   -.0019362
        191  |  -.4618197   .2314486    -2.00   0.049    -.9217031   -.0019362
        193  |  -.4618197   .2314486    -2.00   0.049    -.9217031   -.0019362
        195  |  -.2620107   .2211556    -1.18   0.239    -.7014423    .1774208
        196  |   .4727499   .4132376     1.14   0.256    -.3483445    1.293844
        197  |   .1148555   .4544698     0.25   0.801    -.7881662    1.017877
        198  |  -.1287909   .1205766    -1.07   0.288     -.368374    .1107922
        199  |  -.2184778   .1788295    -1.22   0.225    -.5738083    .1368527
        200  |  -.0313272   .1186626    -0.26   0.792    -.2671072    .2044528
        201  |   .6509794   .1728182     3.77   0.000     .3075933    .9943655
        202  |   .9760187   .1003549     9.73   0.000     .7766157    1.175422
        203  |   .5381803   .2314486     2.33   0.022     .0782969    .9980638
        204  |  -.0471486   .1214724    -0.39   0.699    -.2885117    .1942144
        205  |   .0024056   .1076002     0.02   0.982    -.2113938     .216205
        206  |   .5115671   .4355171     1.17   0.243    -.3537961     1.37693
        207  |   .2146436   .2804816     0.77   0.446    -.3426673    .7719546
        210  |  -.0349301   .1112818    -0.31   0.754    -.2560447    .1861844
        211  |  -.1048032   .1150229    -0.91   0.365    -.3333514    .1237449
        212  |  -.0477672   .1235123    -0.39   0.700    -.2931835     .197649
        213  |   -.065247    .146847    -0.44   0.658    -.3570289     .226535
        214  |  -.1224852   .1534128    -0.80   0.427    -.4273132    .1823428
        215  |   .9522328   .1235123     7.71   0.000     .7068165    1.197649
        216  |   .4823245    .418337     1.15   0.252    -.3489022    1.313551
        217  |   .3299876   .3778115     0.87   0.385    -.4207158    1.080691
        219  |   .3912038   .4013946     0.97   0.332    -.4063588    1.188766
        221  |   .6177041   .3738104     1.65   0.102    -.1250491    1.360457
        227  |  -.0507994   .1409531    -0.36   0.719    -.3308702    .2292714
        231  |  -.0382823   .1269616    -0.30   0.764    -.2905522    .2139877
        233  |   .0043576   .1126554     0.04   0.969    -.2194862    .2282014
        235  |  -.0602433   .1179186    -0.51   0.611    -.2945451    .1740585
        237  |   1.015579   .1340081     7.58   0.000     .7493075     1.28185
        239  |    1.01316   .1217884     8.32   0.000     .7711695    1.255151
        241  |   .0215762   .1214751     0.18   0.859    -.2197923    .2629446
        243  |  -.2411064   .1177663    -2.05   0.044    -.4751055   -.0071072
        244  |  -.0768695   .0976218    -0.79   0.433    -.2708419    .1171029
        245  |   .0215762   .1214751     0.18   0.859    -.2197923    .2629446
        247  |   .5914925   .2984964     1.98   0.051    -.0016135    1.184599
        248  |  -.0834412   .1037152    -0.80   0.423     -.289521    .1226387
        250  |  -.0558719   .1367545    -0.41   0.684    -.3276001    .2158563
        251  |   .1285496   .3188191     0.40   0.688    -.5049373    .7620364
        252  |  -.0501217   .1005175    -0.50   0.619    -.2498478    .1496044
        267  |  -.0591295   .1201109    -0.49   0.624    -.2977873    .1795282
        269  |   .0215762   .1214751     0.18   0.859    -.2197923    .2629446
        271  |  -.2776109   .1704409    -1.63   0.107    -.6162732    .0610514
        272  |  -.1417881   .1227638    -1.15   0.251    -.3857171    .1021409
        274  |  -.0558719   .1367545    -0.41   0.684    -.3276001    .2158563
        275  |  -.0995105   .1777093    -0.56   0.577    -.4526151    .2535941
        276  |  -.0768695   .0976218    -0.79   0.433    -.2708419    .1171029
        278  |  -.0558719   .1367545    -0.41   0.684    -.3276001    .2158563
        279  |  -.0740223   .1715618    -0.43   0.667    -.4149119    .2668673
        280  |  -.0501217   .1005175    -0.50   0.619    -.2498478    .1496044
        283  |   .0307873   .1510472     0.20   0.839    -.2693402    .3309149
        284  |   .8450686   .1192916     7.08   0.000     .6080388    1.082098
        285  |  -.0832335   .1393025    -0.60   0.552    -.3600247    .1935577
        287  |  -.0591295   .1201109    -0.49   0.624    -.2977873    .1795282
             |
      fYes_T |   .1048096   .0559098     1.87   0.064     -.006282    .2159013
        mage |  -.0044042   .0030639    -1.44   0.154     -.010492    .0016837
    mmarried |   .0546063   .0633838     0.86   0.391     -.071336    .1805485
       makan |  -.0561309   .0629836    -0.89   0.375     -.181278    .0690162
mselfemplo~d |  -.1186831   .0551015    -2.15   0.034    -.2281687   -.0091975
       m2q1a |   .0062544    .016748     0.37   0.710    -.0270236    .0395324
      2.m3q1 |  -.0990429   .0713258    -1.39   0.168    -.2407658      .04268
        trt2 |  -.0147846    .113073    -0.13   0.896    -.2394583    .2098891
             |
      c.trt2#|
          c. |
mkt_c_frac~o |  -1.227113    .704151    -1.74   0.085    -2.626246    .1720204
             |
        trt3 |  -.0331126   .1011768    -0.33   0.744    -.2341487    .1679234
             |
      c.trt3#|
          c. |
mkt_c_frac~o |  -1.344485   .6105823    -2.20   0.030    -2.557699   -.1312709
             |
        trt4 |  -.0761161   .1016509    -0.75   0.456    -.2780944    .1258621
             |
      c.trt4#|
          c. |
mkt_c_frac~o |  -.8780151    .635034    -1.38   0.170    -2.139814    .3837838
             |
mkt_c_frac~o |    1.12451   .5310466     2.12   0.037      .069332    2.179688
       _cons |   .2152244   .1388016     1.55   0.125    -.0605714    .4910203
------------------------------------------------------------------------------

. reg fdamt i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a i
> .m3q1 trt2 c.trt2#c.mkt_c_fracAnyEducNo trt3 c.trt3#c.mkt_c_fracAnyEducNo tr
> t4 c.trt4#c.mkt_c_fracAnyEducNo mkt_c_fracAnyEducNo, r cluster(ge02) level(9
> 5)

Linear regression                               Number of obs     =        332
                                                F(80, 89)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6907
                                                Root MSE          =      .9659

                                  (Std. err. adjusted for 90 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
       fdamt | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |   .0990545   .4405738     0.22   0.823    -.7763561    .9744652
          5  |   2.040636   1.421425     1.44   0.155    -.7837051    4.864976
          6  |  -.3133703   .5648998    -0.55   0.580    -1.435814    .8090735
          7  |   .0990545   .4405738     0.22   0.823    -.7763561    .9744652
          8  |   .1126699    .342416     0.33   0.743    -.5677035    .7930432
         11  |   .1271592   .4083634     0.31   0.756    -.6842502    .9385686
         14  |   .1271592   .4083634     0.31   0.756    -.6842502    .9385686
         18  |  -.3133703   .5648998    -0.55   0.580    -1.435814    .8090735
         22  |   .0990545   .4405738     0.22   0.823    -.7763561    .9744652
         23  |   .0356032   .4660884     0.08   0.939    -.8905045     .961711
         24  |  -.3133703   .5648998    -0.55   0.580    -1.435814    .8090735
         26  |  -.1268897   .4615983    -0.27   0.784    -1.044076    .7902963
         27  |  -.3133703   .5648998    -0.55   0.580    -1.435814    .8090735
         29  |  -.3122189   .4972818    -0.63   0.532    -1.300307    .6758695
         30  |  -.3133703   .5648998    -0.55   0.580    -1.435814    .8090735
         32  |   .1195571   .3699977     0.32   0.747    -.6156205    .8547347
         33  |  -.3133703   .5648998    -0.55   0.580    -1.435814    .8090735
         34  |   .0990545   .4405738     0.22   0.823    -.7763561    .9744652
         35  |    -.06565   .3181879    -0.21   0.837    -.6978825    .5665826
         37  |  -.2769186   .4183069    -0.66   0.510    -1.108085    .5542483
         38  |   .4723618   .7249633     0.65   0.516    -.9681248    1.912848
         39  |  -.2993577   .4134558    -0.72   0.471    -1.120885    .5221701
         40  |   1.029644   .8792033     1.17   0.245    -.7173138    2.776603
         41  |  -.2073663   .4499232    -0.46   0.646    -1.101354    .6866216
         42  |  -.0561117   .6692703    -0.08   0.933    -1.385938    1.273714
         51  |  -.2898735   .3837259    -0.76   0.452    -1.052329    .4725818
         52  |  -.4909074   .4729509    -1.04   0.302    -1.430651     .448836
         53  |  -.4337524   .4404826    -0.98   0.327    -1.308982    .4414772
         54  |   .1344603   .6325987     0.21   0.832      -1.1225     1.39142
         55  |  -.2175503   .4359014    -0.50   0.619    -1.083677    .6485766
         56  |   .2708816   .5229712     0.52   0.606    -.7682511    1.310014
         57  |   1.315692   1.043372     1.26   0.211    -.7574654     3.38885
         58  |   .7162791   .4575309     1.57   0.121    -.1928251    1.625383
         61  |  -.1637735   .4453944    -0.37   0.714    -1.048763    .7212156
         62  |  -.2671073    .462498    -0.58   0.565    -1.186081    .6518665
         63  |   -.401446   .5568924    -0.72   0.473    -1.507979    .7050873
         64  |  -.2071722   .4490957    -0.46   0.646    -1.099516    .6851713
         65  |   .1044087   .6010559     0.17   0.862    -1.089877    1.298694
         66  |   3.919483   .4288211     9.14   0.000     3.067424    4.771541
         67  |    2.06047   .9956796     2.07   0.041     .0820758    4.038864
         68  |  -.2071722   .4490957    -0.46   0.646    -1.099516    .6851713
         69  |   .5643638   .4607294     1.22   0.224    -.3510957    1.479823
         71  |   .2063707   .4296748     0.48   0.632    -.6473841    1.060125
         72  |  -.2071722   .4490957    -0.46   0.646    -1.099516    .6851713
         89  |   .3102812   .5081509     0.61   0.543    -.6994037    1.319966
         90  |  -.0047178    .417284    -0.01   0.991    -.8338523    .8244167
         91  |  -.4065702   .5405824    -0.75   0.454    -1.480696    .6675555
         93  |    .378768   .4172396     0.91   0.366    -.4502781    1.207814
         94  |  -.2851492   .4788492    -0.60   0.553    -1.236612     .666314
         95  |  -.6693172   .6211297    -1.08   0.284    -1.903489    .5648543
         97  |   .0510866   .4282304     0.12   0.905     -.799798    .9019712
         98  |  -.2671073    .462498    -0.58   0.565    -1.186081    .6518665
         99  |  -.4100867   .6071795    -0.68   0.501    -1.616539    .7963659
        102  |   .3617253   .6467314     0.56   0.577    -.9233164    1.646767
        103  |   1.270948   .4061949     3.13   0.002     .4638475    2.078049
        104  |   .3835639   .5055819     0.76   0.450    -.6210166    1.388144
        107  |   .0531186   .4694061     0.11   0.910    -.8795814    .9858185
        109  |  -.4354314   .4793059    -0.91   0.366    -1.387802    .5169393
        110  |  -.0154264    .469108    -0.03   0.974     -.947534    .9166812
        113  |  -.1747953   .4519282    -0.39   0.700    -1.072767    .7231764
        114  |  -.1860636   .4368682    -0.43   0.671    -1.054111    .6819843
        117  |  -.1747953   .4519282    -0.39   0.700    -1.072767    .7231764
        118  |  -.1788645   .5133971    -0.35   0.728    -1.198973    .8412446
        137  |  -.5840592   .4786003    -1.22   0.226    -1.535028    .3669093
        138  |  -.1595671   .4747308    -0.34   0.738    -1.102847     .783713
        141  |  -.6960675     .45894    -1.52   0.133    -1.607972    .2158366
        142  |   .2238942      .4949     0.45   0.652    -.7594615     1.20725
        145  |  -.2868036   .4150139    -0.69   0.491    -1.111427    .5378202
        146  |  -.0898681   .4822495    -0.19   0.853    -1.048088    .8683513
        149  |   .7692006   1.020931     0.75   0.453    -1.259369     2.79777
        150  |   .8176925    .405236     2.02   0.047     .0124972    1.622888
        153  |  -.1747953   .4519282    -0.39   0.700    -1.072767    .7231764
        154  |   .0028472   .4349609     0.01   0.995    -.8614107    .8671052
        157  |  -.2671494   .5607578    -0.48   0.635    -1.381363    .8470644
        158  |  -.2869301   .8107686    -0.35   0.724     -1.89791     1.32405
        159  |   .0625258   .4487404     0.14   0.889    -.8291118    .9541633
        160  |   3.179608    .613278     5.18   0.000     1.961038    4.398179
        162  |   .1796085    .613278     0.29   0.770    -1.038962    1.398179
        171  |  -.5968246    .551246    -1.08   0.282    -1.692139    .4984895
        172  |   .2465313    .683394     0.36   0.719    -1.111358    1.604421
        173  |  -.5968246    .551246    -1.08   0.282    -1.692139    .4984895
        174  |  -.8203915    .613278    -1.34   0.184    -2.038962    .3981788
        175  |  -.2671494   .5607578    -0.48   0.635    -1.381363    .8470644
        176  |  -.8203915    .613278    -1.34   0.184    -2.038962    .3981788
        177  |   .6532619   .4952753     1.32   0.191    -.3308397    1.637363
        178  |   .2130699   .6123439     0.35   0.729    -1.003644    1.429784
        180  |    1.21307   1.142141     1.06   0.291    -1.056339    3.482479
        181  |  -.5254443    .453252    -1.16   0.249    -1.426046    .3751577
        182  |   -.100705   .4492863    -0.22   0.823    -.9934274    .7920174
        183  |   2.600184    .750139     3.47   0.001     1.109673    4.090694
        184  |   1.051163   1.640378     0.64   0.523    -2.208234    4.310559
        185  |  -.9144262   .5772946    -1.58   0.117    -2.061498    .2326459
        186  |   -.022159   .5362659    -0.04   0.967    -1.087708     1.04339
        187  |  -1.399816    .750139    -1.87   0.065    -2.890327    .0906938
        189  |  -1.399816    .750139    -1.87   0.065    -2.890327    .0906938
        191  |  -1.399816    .750139    -1.87   0.065    -2.890327    .0906938
        193  |  -1.399816    .750139    -1.87   0.065    -2.890327    .0906938
        195  |  -.7681394    .754802    -1.02   0.312    -2.267915     .731636
        196  |   2.311227   2.174443     1.06   0.291    -2.009345    6.631799
        197  |  -.3471471   .7637677    -0.45   0.651    -1.864737    1.170443
        198  |  -.5479265   .4438338    -1.23   0.220    -1.429815    .3339618
        199  |  -.6804804   .6256202    -1.09   0.280    -1.923574    .5626136
        200  |  -.2125104   .4268796    -0.50   0.620    -1.060711    .6356902
        201  |   1.842879   .6069937     3.04   0.003     .6367951    3.048962
        202  |   4.861337   .3983726    12.20   0.000     4.069779    5.652895
        203  |   3.600184    .750139     4.80   0.000     2.109673    5.090694
        204  |  -.2577971   .4532601    -0.57   0.571    -1.158415     .642821
        205  |    .021102   .4037678     0.05   0.958    -.7811759      .82338
        206  |   1.970717   1.743403     1.13   0.261    -1.493388    5.434822
        207  |   1.089095   1.389362     0.78   0.435    -1.671538    3.849729
        210  |  -.0992035   .4370178    -0.23   0.821    -.9675486    .7691417
        211  |  -.3720461   .4569102    -0.81   0.418    -1.279917    .5358247
        212  |  -.2285799   .4574099    -0.50   0.619    -1.137444    .6802838
        213  |  -.2391813   .5273674    -0.45   0.651    -1.287049    .8086865
        214  |  -.4872965   .5202864    -0.94   0.352    -1.521094    .5465016
        215  |   .7714201   .4574099     1.69   0.095    -.1374436    1.680284
        216  |   1.421967   1.268989     1.12   0.265    -1.099487    3.943422
        217  |    1.52813   1.889308     0.81   0.421    -2.225885    5.282145
        219  |   1.563685    1.56676     1.00   0.321    -1.549434    4.676805
        221  |   .4907704   .5346791     0.92   0.361    -.5716257    1.553166
        227  |  -.2820893   .5179028    -0.54   0.587    -1.311151    .7469726
        231  |  -.2749579   .4813782    -0.57   0.569    -1.231446    .6815303
        233  |  -.0759622   .4009192    -0.19   0.850    -.8725802    .7206558
        235  |  -.3200807   .4021684    -0.80   0.428    -1.119181    .4790193
        237  |   1.992514     1.0043     1.98   0.050    -.0030086    3.988037
        239  |   6.016404   1.191547     5.05   0.000     3.648826    8.383983
        241  |  -.0515628   .4490738    -0.11   0.909    -.9438629    .8407374
        243  |  -.6789244   .4365061    -1.56   0.123    -1.546253     .188404
        244  |  -.1989544   .3618852    -0.55   0.584    -.9180125    .5201038
        245  |  -.0515628   .4490738    -0.11   0.909    -.9438629    .8407374
        247  |    2.49526   1.292697     1.93   0.057    -.0733022    5.063823
        248  |   -.208718   .3859528    -0.54   0.590     -.975598     .558162
        250  |  -.2862196   .4831358    -0.59   0.555      -1.2462     .673761
        251  |  -.3082084   .4542803    -0.68   0.499    -1.210854    .5944369
        252  |  -.1882722   .3894723    -0.48   0.630    -.9621453    .5856009
        267  |   -.245505   .4549222    -0.54   0.591    -1.149426    .6584158
        269  |  -.0515628   .4490738    -0.11   0.909    -.9438629    .8407374
        271  |  -.8395601   .5590804    -1.50   0.137    -1.950441    .2713209
        272  |  -.5780088   .4418508    -1.31   0.194    -1.455957    .2999393
        274  |  -.2862196   .4831358    -0.59   0.555      -1.2462     .673761
        275  |  -.2784969   .5897051    -0.47   0.638    -1.450228    .8932347
        276  |  -.1989544   .3618852    -0.55   0.584    -.9180125    .5201038
        278  |  -.2862196   .4831358    -0.59   0.555      -1.2462     .673761
        279  |   -.332134   .6129132    -0.54   0.589     -1.54998    .8857116
        280  |  -.1882722   .3894723    -0.48   0.630    -.9621453    .5856009
        283  |   .0771299   .5701693     0.14   0.893    -1.055784    1.210044
        284  |   .4024639   .4265288     0.94   0.348    -.4450397    1.249968
        285  |  -.4608266   .4763704    -0.97   0.336    -1.407364    .4857111
        287  |   -.245505   .4549222    -0.54   0.591    -1.149426    .6584158
             |
      fYes_T |   .4092639   .1823211     2.24   0.027     .0469958     .771532
        mage |    -.01307   .0099604    -1.31   0.193     -.032861    .0067211
    mmarried |   .2462248   .2335295     1.05   0.295    -.2177933    .7102429
       makan |  -.1378072   .1999099    -0.69   0.492    -.5350238    .2594095
mselfemplo~d |  -.4850939   .2330498    -2.08   0.040    -.9481588   -.0220289
       m2q1a |   .0353548    .047342     0.75   0.457    -.0587126    .1294223
      2.m3q1 |   -.260462   .2344763    -1.11   0.270    -.7263614    .2054373
        trt2 |  -.0843158   .3749733    -0.22   0.823    -.8293796    .6607481
             |
      c.trt2#|
          c. |
mkt_c_frac~o |  -2.228824   2.112524    -1.06   0.294    -6.426365    1.968717
             |
        trt3 |  -.0162296   .3074889    -0.05   0.958    -.6272035    .5947442
             |
      c.trt3#|
          c. |
mkt_c_frac~o |  -4.215057   2.159195    -1.95   0.054    -8.505331    .0752173
             |
        trt4 |  -.1125444   .3248961    -0.35   0.730    -.7581059    .5330172
             |
      c.trt4#|
          c. |
mkt_c_frac~o |  -2.875124   1.625593    -1.77   0.080    -6.105143    .3548945
             |
mkt_c_frac~o |   3.720054   1.801627     2.06   0.042     .1402607    7.299848
       _cons |   .5618093   .4877238     1.15   0.252    -.4072875    1.530906
------------------------------------------------------------------------------

. 
. 
. *VENDORS - base bundling effects
. reg fd i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a trt 
> c.trt#c.bundle bundle, r cluster(ge02) level(95)

Linear regression                               Number of obs     =        332
                                                F(75, 89)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6994
                                                Root MSE          =      .2884

                                  (Std. err. adjusted for 90 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
          fd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.0645451   .1377288    -0.47   0.640    -.3382093    .2091191
          5  |   .4134777   .3627702     1.14   0.257     -.307339    1.134294
          6  |  -.0040484   .1582139    -0.03   0.980    -.3184161    .3103193
          7  |  -.0645451   .1377288    -0.47   0.640    -.3382093    .2091191
          8  |   .0307527   .0868483     0.35   0.724    -.1418131    .2033185
         11  |   .0722307   .1058927     0.68   0.497    -.1381759    .2826373
         14  |   .0722307   .1058927     0.68   0.497    -.1381759    .2826373
         18  |  -.0040484   .1582139    -0.03   0.980    -.3184161    .3103193
         22  |  -.0645451   .1377288    -0.47   0.640    -.3382093    .2091191
         23  |   .0051199   .1068073     0.05   0.962     -.207104    .2173437
         24  |  -.0040484   .1582139    -0.03   0.980    -.3184161    .3103193
         26  |  -.0688838    .158457    -0.43   0.665    -.3837345    .2459668
         27  |  -.0040484   .1582139    -0.03   0.980    -.3184161    .3103193
         29  |  -.1597561   .1795051    -0.89   0.376    -.5164289    .1969168
         30  |  -.0040484   .1582139    -0.03   0.980    -.3184161    .3103193
         32  |   .1221056   .1823626     0.67   0.505    -.2402449    .4844562
         33  |  -.0040484   .1582139    -0.03   0.980    -.3184161    .3103193
         34  |  -.0645451   .1377288    -0.47   0.640    -.3382093    .2091191
         35  |   .0102985   .0823774     0.13   0.901    -.1533836    .1739806
         37  |  -.0564929   .0920663    -0.61   0.541    -.2394266    .1264408
         38  |   .0604869   .1727106     0.35   0.727    -.2826855    .4036592
         39  |   -.038474   .0944744    -0.41   0.685    -.2261925    .1492446
         40  |   .2507774   .2297313     1.09   0.278    -.2056937    .7072485
         41  |   .0851033   .2076335     0.41   0.683    -.3274601    .4976667
         42  |   .3607773   .2913673     1.24   0.219    -.2181634     .939718
         51  |  -.0763675   .0841504    -0.91   0.367    -.2435724    .0908375
         52  |  -.1914883   .1193819    -1.60   0.112    -.4286975    .0457209
         53  |  -.0625676   .0924563    -0.68   0.500    -.2462763    .1211411
         54  |   .0337816    .186874     0.18   0.857     -.337533    .4050962
         55  |  -.0253127   .1052432    -0.24   0.810    -.2344286    .1838033
         56  |   .0683559   .1595053     0.43   0.669    -.2485776    .3852895
         57  |   .8315207   .1044558     7.96   0.000     .6239692    1.039072
         58  |   .8379084   .1526867     5.49   0.000     .5345232    1.141294
         61  |  -.0402376   .1048829    -0.38   0.702    -.2486376    .1681624
         62  |  -.0910985   .1084339    -0.84   0.403    -.3065544    .1243574
         63  |  -.0535753   .1348465    -0.40   0.692    -.3215125    .2143619
         64  |   -.191069   .1273915    -1.50   0.137    -.4441933    .0620553
         65  |   .0124886   .1535347     0.08   0.935    -.2925816    .3175589
         66  |   .9220451   .1389899     6.63   0.000     .6458751    1.198215
         67  |    .460372   .2201826     2.09   0.039      .022874    .8978701
         68  |   -.191069   .1273915    -1.50   0.137    -.4441933    .0620553
         69  |   .1796106   .1331121     1.35   0.181    -.0848802    .4441015
         71  |   .3740557   .2565986     1.46   0.148    -.1358002    .8839117
         72  |   -.191069   .1273915    -1.50   0.137    -.4441933    .0620553
         89  |   .0814647   .1356244     0.60   0.550     -.188018    .3509474
         90  |  -.0387772   .1334919    -0.29   0.772    -.3040228    .2264685
         91  |  -.1310753   .1879866    -0.70   0.487    -.5046006      .24245
         93  |    .107374   .1057012     1.02   0.312     -.102652       .3174
         94  |  -.1298164   .1474938    -0.88   0.381    -.4228834    .1632506
         95  |  -.2132688   .1752859    -1.22   0.227    -.5615582    .1350206
         97  |   .0218742   .1143784     0.19   0.849    -.2053932    .2491415
         98  |  -.0910985   .1084339    -0.84   0.403    -.3065544    .1243574
         99  |  -.1196293   .1547762    -0.77   0.442    -.4271662    .1879076
        102  |   .4215854   .5389152     0.78   0.436    -.6492277    1.492398
        103  |   1.107034    .100101    11.06   0.000     .9081351    1.305932
        104  |   .7052079   .1465265     4.81   0.000     .4140629     .996353
        107  |   .0597813    .097709     0.61   0.542    -.1343643    .2539269
        109  |  -.1434545   .1089066    -1.32   0.191    -.3598495    .0729405
        110  |  -.0397209     .10602    -0.37   0.709    -.2503803    .1709386
        113  |  -.0835961   .0854644    -0.98   0.331    -.2534121    .0862199
        114  |  -.1174674   .1602276    -0.73   0.465    -.4358362    .2009015
        117  |  -.0835961   .0854644    -0.98   0.331    -.2534121    .0862199
        118  |  -.1544706    .103674    -1.49   0.140    -.3604687    .0515275
        137  |  -.1873192   .1018475    -1.84   0.069    -.3896879    .0150495
        138  |  -.0963148   .1081352    -0.89   0.375    -.3111772    .1185476
        141  |   -.203313   .1102446    -1.84   0.068    -.4223666    .0157407
        142  |  -.0957626   .1295459    -0.74   0.462    -.3531675    .1616423
        145  |  -.0995899   .0908954    -1.10   0.276    -.2801971    .0810173
        146  |  -.0905522   .1152922    -0.79   0.434    -.3196354    .1385311
        149  |    .408407   .4915723     0.83   0.408    -.5683366    1.385151
        150  |    .893823   .1238667     7.22   0.000     .6477026    1.139944
        153  |  -.0835961   .0854644    -0.98   0.331    -.2534121    .0862199
        154  |   .0065766   .1110903     0.06   0.953    -.2141574    .2273106
        157  |  -.0317495   .1234145    -0.26   0.798    -.2769714    .2134725
        158  |  -.0653097   .2641586    -0.25   0.805    -.5901872    .4595678
        159  |   .0331537   .0875518     0.38   0.706    -.1408099    .2071173
        160  |   .7069771   .1722954     4.10   0.000     .3646298    1.049324
        162  |   .7069771   .1722954     4.10   0.000     .3646298    1.049324
        171  |  -.0966526   .1556863    -0.62   0.536    -.4059981    .2126928
        172  |   .1624034   .1335565     1.22   0.227    -.1029704    .4277773
        173  |  -.0966526   .1556863    -0.62   0.536    -.4059981    .2126928
        174  |  -.2930229   .1722954    -1.70   0.092    -.6353702    .0493245
        175  |  -.0317495   .1234145    -0.26   0.798    -.2769714    .2134725
        176  |  -.2930229   .1722954    -1.70   0.092    -.6353702    .0493245
        177  |   .9294306    .101895     9.12   0.000     .7269673    1.131894
        178  |   .4346903    .302225     1.44   0.154    -.1658244    1.035205
        180  |   .4346903    .302225     1.44   0.154    -.1658244    1.035205
        181  |  -.1940003   .1352132    -1.43   0.155    -.4626661    .0746655
        182  |  -.0512989   .1071199    -0.48   0.633    -.2641439    .1615461
        183  |   .9386052   .1309832     7.17   0.000     .6783445    1.198866
        184  |    .240162   .3965917     0.61   0.546    -.5478572    1.028181
        185  |  -.2096943   .1179113    -1.78   0.079    -.4439816     .024593
        186  |  -.0459933   .1230455    -0.37   0.709     -.290482    .1984954
        187  |  -.0613948   .1309832    -0.47   0.640    -.3216555    .1988659
        189  |  -.0613948   .1309832    -0.47   0.640    -.3216555    .1988659
        191  |  -.0613948   .1309832    -0.47   0.640    -.3216555    .1988659
        193  |  -.0613948   .1309832    -0.47   0.640    -.3216555    .1988659
        195  |  -.1198505   .1315059    -0.91   0.365    -.3811499    .1414488
        196  |   .4325664   .4282285     1.01   0.315    -.4183145    1.283447
        197  |   .2181092    .408921     0.53   0.595    -.5944081    1.030627
        198  |  -.1432581   .1072174    -1.34   0.185    -.3562968    .0697805
        199  |  -.1152241   .1081514    -1.07   0.290    -.3301185    .0996703
        200  |  -.0742887   .1054387    -0.70   0.483    -.2837931    .1352157
        201  |   .8644554   .1253653     6.90   0.000     .6153573    1.113554
        202  |   .9604649   .0861753    11.15   0.000     .7892364    1.131693
        203  |   .9386052   .1309832     7.17   0.000     .6783445    1.198866
        204  |  -.0619102   .1073567    -0.58   0.566    -.2752257    .1514053
        205  |  -.0378685   .1010269    -0.37   0.709    -.2386069    .1628699
        206  |   .4713856   .4366373     1.08   0.283    -.3962034    1.338975
        207  |   .1994996   .2673994     0.75   0.458    -.3318173    .7308165
        210  |  -.0434408   .1031901    -0.42   0.675    -.2484774    .1615958
        211  |  -.1125895   .1107544    -1.02   0.312    -.3326561    .1074771
        212  |  -.0898158   .0966382    -0.93   0.355    -.2818339    .1022023
        213  |  -.0717886   .1268905    -0.57   0.573    -.3239174    .1803402
        214  |  -.1323779   .1328303    -1.00   0.322     -.396309    .1315532
        215  |   .9101842   .0966382     9.42   0.000     .7181661    1.102202
        216  |   .4713452   .4361881     1.08   0.283    -.3953514    1.338042
        217  |   .2773757   .3681462     0.75   0.453     -.454123    1.008874
        219  |   .3644978   .3904236     0.93   0.353    -.4112655    1.140261
        221  |   .5970254   .3908951     1.53   0.130    -.1796749    1.373726
        227  |  -.1068398      .1194    -0.89   0.373    -.3440851    .1304056
        231  |   -.090532   .1197292    -0.76   0.452    -.3284314    .1473674
        233  |  -.0323379    .105676    -0.31   0.760    -.2423139    .1776381
        235  |  -.1027909     .10499    -0.98   0.330    -.3114039    .1058221
        237  |   .9879824   .1227644     8.05   0.000     .7440522    1.231913
        239  |   .9804514   .1057845     9.27   0.000     .7702599    1.190643
        241  |  -.0394542   .0901636    -0.44   0.663    -.2186074    .1396989
        243  |  -.2222057   .1250865    -1.78   0.079    -.4707498    .0263385
        244  |  -.0729148    .081976    -0.89   0.376    -.2357993    .0899697
        245  |  -.0394542   .0901636    -0.44   0.663    -.2186074    .1396989
        247  |   .5382509   .2987012     1.80   0.075     -.055262    1.131764
        248  |  -.0696433    .082652    -0.84   0.402    -.2338711    .0945845
        250  |  -.0993474   .1155325    -0.86   0.392     -.328908    .1302132
        251  |   .0727511   .3499434     0.21   0.836    -.6225789    .7680812
        252  |  -.0492283   .0866552    -0.57   0.571    -.2214104    .1229537
        267  |  -.0963588   .0905785    -1.06   0.290    -.2763362    .0836187
        269  |  -.0394542   .0901636    -0.44   0.663    -.2186074    .1396989
        271  |  -.3426939   .1240391    -2.76   0.007     -.589157   -.0962308
        272  |  -.1594944   .1074807    -1.48   0.141    -.3730563    .0540675
        274  |  -.0993474   .1155325    -0.86   0.392     -.328908    .1302132
        275  |  -.2087412   .1730164    -1.21   0.231    -.5525212    .1350388
        276  |  -.0729148    .081976    -0.89   0.376    -.2357993    .0899697
        278  |  -.0993474   .1155325    -0.86   0.392     -.328908    .1302132
        279  |  -.1287826   .1312378    -0.98   0.329    -.3895492     .131984
        280  |  -.0492283   .0866552    -0.57   0.571    -.2214104    .1229537
        283  |  -.0250595   .1133096    -0.22   0.825    -.2502033    .2000842
        284  |   .8470486   .1084045     7.81   0.000     .6316511    1.062446
        285  |  -.1431773   .1116523    -1.28   0.203    -.3650281    .0786735
        287  |  -.0963588   .0905785    -1.06   0.290    -.2763362    .0836187
             |
      fYes_T |   .1037231   .0570699     1.82   0.073    -.0096736    .2171198
        mage |  -.0032311   .0031417    -1.03   0.307    -.0094737    .0030115
    mmarried |   .0655927   .0724098     0.91   0.367    -.0782839    .2094694
       makan |  -.0406683   .0619677    -0.66   0.513    -.1637968    .0824602
mselfemplo~d |  -.1199809   .0527269    -2.28   0.025    -.2247481   -.0152137
       m2q1a |   .0063813   .0170926     0.37   0.710    -.0275812    .0403439
         trt |  -.1386096   .0925903    -1.50   0.138    -.3225844    .0453653
             |
       c.trt#|
    c.bundle |  -.3500177    .174024    -2.01   0.047    -.6957996   -.0042358
             |
      bundle |   .2348592   .1629915     1.44   0.153    -.0890014    .5587197
       _cons |   .2900586   .1312642     2.21   0.030     .0292394    .5508777
------------------------------------------------------------------------------

. reg fdamt i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a t
> rt c.trt#c.bundle bundle, r cluster(ge02) level(95)

Linear regression                               Number of obs     =        332
                                                F(75, 89)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6710
                                                Root MSE          =     .98161

                                  (Std. err. adjusted for 90 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
       fdamt | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |   -.140587   .4076957    -0.34   0.731    -.9506696    .6694956
          5  |   1.942753    1.50774     1.29   0.201    -1.053094      4.9386
          6  |  -.1268558   .4896333    -0.26   0.796    -1.099747    .8460352
          7  |   -.140587   .4076957    -0.34   0.731    -.9506696    .6694956
          8  |   .0200315   .2718009     0.07   0.941     -.520031    .5600941
         11  |   .1178677   .3367587     0.35   0.727    -.5512647    .7870002
         14  |   .1178677   .3367587     0.35   0.727    -.5512647    .7870002
         18  |  -.1268558   .4896333    -0.26   0.796    -1.099747    .8460352
         22  |   -.140587   .4076957    -0.34   0.731    -.9506696    .6694956
         23  |  -.0712355   .3293182    -0.22   0.829    -.7255839    .5831128
         24  |  -.1268558   .4896333    -0.26   0.796    -1.099747    .8460352
         26  |   -.255581   .4336323    -0.59   0.557    -1.117199    .6060372
         27  |  -.1268558   .4896333    -0.26   0.796    -1.099747    .8460352
         29  |  -.4905061   .4712917    -1.04   0.301    -1.426953    .4459404
         30  |  -.1268558   .4896333    -0.26   0.796    -1.099747    .8460352
         32  |  -.0024462   .3402935    -0.01   0.994    -.6786021    .6737097
         33  |  -.1268558   .4896333    -0.26   0.796    -1.099747    .8460352
         34  |   -.140587   .4076957    -0.34   0.731    -.9506696    .6694956
         35  |   -.061997   .2678658    -0.23   0.817    -.5942407    .4702467
         37  |  -.2230008   .3113289    -0.72   0.476    -.8416048    .3956031
         38  |   .4061711    .776825     0.52   0.602    -1.137364    1.949706
         39  |  -.1474072   .3165218    -0.47   0.643    -.7763293    .4815149
         40  |   .9955026   .9039759     1.10   0.274    -.8006782    2.791683
         41  |  -.1486973   .3470706    -0.43   0.669    -.8383191    .5409244
         42  |   .0048724   .4999099     0.01   0.992    -.9884378    .9981827
         51  |   -.272816    .282568    -0.97   0.337    -.8342726    .2886407
         52  |  -.6444682   .3950826    -1.63   0.106    -1.429489    .1405525
         53  |  -.2657015   .3101596    -0.86   0.394    -.8819819     .350579
         54  |  -.0128659   .5917376    -0.02   0.983    -1.188636    1.162904
         55  |  -.1060018   .3529747    -0.30   0.765     -.807355    .5953514
         56  |   .1176886    .472303     0.25   0.804    -.8207675    1.056145
         57  |   1.372866   1.063991     1.29   0.200    -.7412617    3.486994
         58  |   .4832855   .4841734     1.00   0.321    -.4787569    1.445328
         61  |  -.2121735   .3485154    -0.61   0.544    -.9046661    .4803192
         62  |  -.2909889   .3940696    -0.74   0.462    -1.073997     .492019
         63  |  -.2500736   .4200802    -0.60   0.553    -1.084764    .5846169
         64  |   -.569475    .412634    -1.38   0.171     -1.38937    .2504201
         65  |  -.0077467   .5131992    -0.02   0.988    -1.027463    1.011969
         66  |   3.795973   .4459435     8.51   0.000     2.909892    4.682053
         67  |   1.961803   .9614219     2.04   0.044      .051478    3.872128
         68  |   -.569475    .412634    -1.38   0.171     -1.38937    .2504201
         69  |   .4520068   .4201707     1.08   0.285    -.3828635    1.286877
         71  |   .2765173   .3513585     0.79   0.433    -.4216247    .9746592
         72  |   -.569475    .412634    -1.38   0.171     -1.38937    .2504201
         89  |   .2139284   .4205656     0.51   0.612    -.6217264    1.049583
         90  |  -.0457679   .3993032    -0.11   0.909    -.8391749    .7476391
         91  |  -.4629813    .564585    -0.82   0.414      -1.5848    .6588371
         93  |   .2680762   .3419832     0.78   0.435     -.411437    .9475895
         94  |  -.3998591   .4545446    -0.88   0.381     -1.30303    .5033113
         95  |  -.7057035   .5245591    -1.35   0.182    -1.747991    .3365842
         97  |  -.0247073   .3551826    -0.07   0.945    -.7304476     .681033
         98  |  -.2909889   .3940696    -0.74   0.462    -1.073997     .492019
         99  |  -.4192146   .4766824    -0.88   0.382    -1.366372    .5279431
        102  |   .2465836   .6869907     0.36   0.720    -1.118452     1.61162
        103  |   1.285437   .3061397     4.20   0.000      .677144     1.89373
        104  |   .0388613   .4749889     0.08   0.935    -.9049315    .9826542
        107  |    .095513   .3420673     0.28   0.781    -.5841675    .7751934
        109  |  -.5064869   .3847321    -1.32   0.191    -1.270941    .2579676
        110  |  -.1647488   .3375915    -0.49   0.627     -.835536    .5060384
        113  |  -.2738024   .2903699    -0.94   0.348    -.8507612    .3031564
        114  |  -.3668636   .4885137    -0.75   0.455     -1.33753    .6038027
        117  |  -.2738024   .2903699    -0.94   0.348    -.8507612    .3031564
        118  |  -.4690913   .3554228    -1.32   0.190    -1.175309    .2371263
        137  |  -.6654661   .3398524    -1.96   0.053    -1.340746    .0098135
        138  |  -.3304146   .3601328    -0.92   0.361    -1.045991    .3851617
        141  |  -.7391714   .3667447    -2.02   0.047    -1.467885   -.0104574
        142  |  -.1961729    .407882    -0.48   0.632    -1.006626    .6142798
        145  |  -.3475077   .3014823    -1.15   0.252    -.9465467    .2515312
        146  |  -.3056633   .3626067    -0.84   0.402    -1.026155    .4148285
        149  |   .6893449   .9912271     0.70   0.489    -1.280202    2.658892
        150  |   .6641568   .3911341     1.70   0.093    -.1130182    1.441332
        153  |  -.2738024   .2903699    -0.94   0.348    -.8507612    .3031564
        154  |  -.0244216   .3835692    -0.06   0.949    -.7865655    .7377223
        157  |  -.0576356   .4056632    -0.14   0.887    -.8636797    .7484086
        158  |  -.1982804    .689931    -0.29   0.774    -1.569159    1.172598
        159  |   .1339718   .3060505     0.44   0.663     -.474144    .7420876
        160  |   3.236191   .4398694     7.36   0.000     2.362179    4.110202
        162  |   .2361905   .4398694     0.54   0.593    -.6378205    1.110202
        171  |  -.2492429   .5359338    -0.47   0.643    -1.314132     .815646
        172  |   .3672486   .4297197     0.85   0.395    -.4865952    1.221092
        173  |  -.2492429   .5359338    -0.47   0.643    -1.314132     .815646
        174  |  -.7638095   .4398694    -1.74   0.086    -1.637821    .1102016
        175  |  -.0576356   .4056632    -0.14   0.887    -.8636797    .7484086
        176  |  -.7638095   .4398694    -1.74   0.086    -1.637821    .1102016
        177  |   .7423081   .3570092     2.08   0.040     .0329385    1.451678
        178  |   .3017196   .4132351     0.73   0.467    -.5193698    1.122809
        180  |    1.30172   1.005671     1.29   0.199    -.6965276    3.299967
        181  |  -.6385317   .4271064    -1.50   0.138    -1.487183    .2101196
        182  |  -.2397135   .3619979    -0.66   0.510    -.9589956    .4795687
        183  |   3.880457   .3929747     9.87   0.000     3.099624    4.661289
        184  |   .9310258   1.564493     0.60   0.553    -2.177588     4.03964
        185  |  -.7707544   .3878667    -1.99   0.050    -1.541437   -.0000716
        186  |  -.2241486   .4322102    -0.52   0.605    -1.082941    .6346439
        187  |  -.1195434   .3929747    -0.30   0.762    -.9003757     .661289
        189  |  -.1195434   .3929747    -0.30   0.762    -.9003757     .661289
        191  |  -.1195434   .3929747    -0.30   0.762    -.9003757     .661289
        193  |  -.1195434   .3929747    -0.30   0.762    -.9003757     .661289
        195  |  -.3129262   .4108445    -0.76   0.448    -1.129265     .503413
        196  |   2.210554   2.218291     1.00   0.322    -2.197142     6.61825
        197  |  -.0016477   .5006894    -0.00   0.997    -.9965068    .9932114
        198  |  -.5572185   .3456742    -1.61   0.111    -1.244066    .1296287
        199  |   -.334981   .3452133    -0.97   0.334    -1.020913    .3509505
        200  |  -.3190897   .3560739    -0.90   0.373    -1.026601    .3884215
        201  |   2.554851    .459099     5.56   0.000     1.642631    3.467071
        202  |   4.834445   .2941295    16.44   0.000     4.250016    5.418874
        203  |   4.880457   .3929747    12.42   0.000     4.099624    5.661289
        204  |  -.2708431   .3504436    -0.77   0.442    -.9671671    .4254809
        205  |  -.1326379   .3325293    -0.40   0.691    -.7933664    .5280907
        206  |   1.856245   1.776892     1.04   0.299    -1.674402    5.386892
        207  |   .9885925   1.377266     0.72   0.475    -1.748006    3.725192
        210  |  -.1553731   .3478744    -0.45   0.656    -.8465921    .5358458
        211  |  -.4164822   .3832928    -1.09   0.280    -1.178077    .3451124
        212  |   -.326394   .3128102    -1.04   0.300    -.9479412    .2951533
        213  |  -.2947227   .4273947    -0.69   0.492    -1.143947    .5545014
        214  |  -.5301662   .4364646    -1.21   0.228    -1.397412    .3370796
        215  |    .673606   .3128102     2.15   0.034     .0520588    1.295153
        216  |   1.361497   1.300436     1.05   0.298    -1.222441    3.945436
        217  |   1.402214   1.839505     0.76   0.448    -2.252844    5.057271
        219  |   1.469461   1.522469     0.97   0.337    -1.555653    4.494575
        221  |   .4203947   .5350946     0.79   0.434    -.6428269    1.483616
        227  |  -.4477033   .4114334    -1.09   0.279    -1.265213    .3698061
        231  |  -.3950076   .4222827    -0.94   0.352    -1.234074    .4440591
        233  |  -.1808962   .3612283    -0.50   0.618    -.8986492    .5368569
        235  |  -.4267474   .3511008    -1.22   0.227    -1.124377    .2708825
        237  |   1.874908   .9926369     1.89   0.062    -.0974398    3.847257
        239  |   5.873464   1.116099     5.26   0.000     3.655799    8.091128
        241  |  -.1760591   .3044391    -0.58   0.565     -.780973    .4288548
        243  |  -.6355652   .4199287    -1.51   0.134    -1.469955    .1988242
        244  |  -.2513142   .2791133    -0.90   0.370    -.8059063    .3032779
        245  |  -.1760591   .3044391    -0.58   0.565     -.780973    .4288548
        247  |   2.308716    1.29404     1.78   0.078    -.2625131    4.879945
        248  |   -.246027   .2787387    -0.88   0.380    -.7998748    .3078209
        250  |  -.3790206   .3566096    -1.06   0.291    -1.087596     .329555
        251  |  -.4471809   .4450162    -1.00   0.318    -1.331419    .4370568
        252  |  -.1971729   .2974182    -0.66   0.509    -.7881364    .3937907
        267  |  -.3369684   .3002933    -1.12   0.265    -.9336449    .2597081
        269  |  -.1760591   .3044391    -0.58   0.565     -.780973    .4288548
        271  |  -1.002287   .4102909    -2.44   0.017    -1.817526   -.1870479
        272  |  -.5994109   .3484695    -1.72   0.089    -1.291812    .0929906
        274  |  -.3790206   .3566096    -1.06   0.291    -1.087596     .329555
        275  |  -.6024321     .53586    -1.12   0.264    -1.667175    .4623104
        276  |  -.2513142   .2791133    -0.90   0.370    -.8059063    .3032779
        278  |  -.3790206   .3566096    -1.06   0.291    -1.087596     .329555
        279  |  -.4825876   .4336659    -1.11   0.269    -1.344272    .3790973
        280  |  -.1971729   .2974182    -0.66   0.509    -.7881364    .3937907
        283  |  -.0909239   .3783545    -0.24   0.811    -.8427062    .6608584
        284  |   .4111635   .3516362     1.17   0.245    -.2875301    1.109857
        285  |  -.5677228   .3605979    -1.57   0.119    -1.284223    .1487777
        287  |  -.3369684   .3002933    -1.12   0.265    -.9336449    .2597081
             |
      fYes_T |   .3916637   .1808885     2.17   0.033     .0322421    .7510852
        mage |  -.0105393   .0107761    -0.98   0.331    -.0319512    .0108725
    mmarried |   .2533527   .2546236     1.00   0.322     -.252579    .7592845
       makan |  -.1187168   .2036918    -0.58   0.561    -.5234482    .2860145
mselfemplo~d |  -.4183136   .2061685    -2.03   0.045     -.827966   -.0086611
       m2q1a |    .031583   .0475674     0.66   0.508    -.0629324    .1260983
         trt |  -.3617628   .2826452    -1.28   0.204    -.9233729    .1998473
             |
       c.trt#|
    c.bundle |  -.9277588   .4275032    -2.17   0.033    -1.777199    -.078319
             |
      bundle |   .6548055   .3780761     1.73   0.087    -.0964236    1.406035
       _cons |   .8568915   .4543758     1.89   0.063    -.0459435    1.759727
------------------------------------------------------------------------------

. 
. reg fd i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a trt2
>  c.trt2#c.bundle trt3 c.trt3#c.bundle trt4 c.trt4#c.bundle bundle, r cluster
> (ge02) level(95)

Linear regression                               Number of obs     =        332
                                                F(79, 89)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.7042
                                                Root MSE          =     .28947

                                  (Std. err. adjusted for 90 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
          fd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.0170018   .1555924    -0.11   0.913    -.3261606     .292157
          5  |   .4484131   .3743866     1.20   0.234    -.2954851    1.192311
          6  |  -.0365121   .1798498    -0.20   0.840    -.3938699    .3208456
          7  |  -.0170018   .1555924    -0.11   0.913    -.3261606     .292157
          8  |   .0535309   .1186571     0.45   0.653    -.1822383       .2893
         11  |   .0398288   .1356082     0.29   0.770    -.2296219    .3092794
         14  |   .0398288   .1356082     0.29   0.770    -.2296219    .3092794
         18  |  -.0365121   .1798498    -0.20   0.840    -.3938699    .3208456
         22  |  -.0170018   .1555924    -0.11   0.913    -.3261606     .292157
         23  |   .0552404   .1429263     0.39   0.700     -.228751    .3392319
         24  |  -.0365121   .1798498    -0.20   0.840    -.3938699    .3208456
         26  |  -.0601351   .1752479    -0.34   0.732     -.408349    .2880788
         27  |  -.0365121   .1798498    -0.20   0.840    -.3938699    .3208456
         29  |  -.1529435   .1825309    -0.84   0.404    -.5156285    .2097415
         30  |  -.0365121   .1798498    -0.20   0.840    -.3938699    .3208456
         32  |   .1358522   .2022235     0.67   0.503    -.2659615    .5376659
         33  |  -.0365121   .1798498    -0.20   0.840    -.3938699    .3208456
         34  |  -.0170018   .1555924    -0.11   0.913    -.3261606     .292157
         35  |  -.0293688   .1105119    -0.27   0.791    -.2489535     .190216
         37  |  -.0492322    .127325    -0.39   0.700    -.3022243    .2037599
         38  |   .0736854   .1929112     0.38   0.703    -.3096251     .456996
         39  |  -.0485362   .1234757    -0.39   0.695    -.2938797    .1968073
         40  |    .276681   .2435271     1.14   0.259    -.2072021    .7605641
         41  |   .0932902   .2236756     0.42   0.678    -.3511485    .5377289
         42  |   .3716673   .3066859     1.21   0.229    -.2377111    .9810457
         51  |  -.0643552   .1194398    -0.54   0.591    -.3016796    .1729691
         52  |  -.1661506   .1532841    -1.08   0.281    -.4707229    .1384217
         53  |  -.0811157   .1268205    -0.64   0.524    -.3331054    .1708739
         54  |   .0469612   .1991575     0.24   0.814    -.3487604    .4426829
         55  |  -.0234817   .1283504    -0.18   0.855    -.2785113    .2315478
         56  |   .0836164   .1718915     0.49   0.628    -.2579285    .4251612
         57  |   .8270282   .1326424     6.24   0.000     .5634705    1.090586
         58  |    .865767   .1848619     4.68   0.000     .4984504    1.233084
         61  |  -.0316175   .1450875    -0.22   0.828    -.3199033    .2566683
         62  |  -.0508596   .1405827    -0.36   0.718    -.3301944    .2284751
         63  |   .0271909   .1682602     0.16   0.872    -.3071385    .3615202
         64  |  -.1457787     .14942    -0.98   0.332    -.4426731    .1511157
         65  |   .0482582    .179877     0.27   0.789    -.3091536    .4056699
         66  |   .9622247     .15749     6.11   0.000     .6492954    1.275154
         67  |   .4938531   .2317843     2.13   0.036     .0333025    .9544036
         68  |  -.1457787     .14942    -0.98   0.332    -.4426731    .1511157
         69  |   .1548851   .1531326     1.01   0.315    -.1493862    .4591563
         71  |    .462323   .2881841     1.60   0.112    -.1102927    1.034939
         72  |  -.1457787     .14942    -0.98   0.332    -.4426731    .1511157
         89  |   .0996522   .1562528     0.64   0.525    -.2108189    .4101232
         90  |   .0084278   .1538456     0.05   0.956    -.2972602    .3141158
         91  |  -.0641552   .2311188    -0.28   0.782    -.5233834     .395073
         93  |   .1117637   .1382071     0.81   0.421     -.162851    .3863783
         94  |   -.092758    .163591    -0.57   0.572    -.4178099    .2322939
         95  |  -.1842757    .210207    -0.88   0.383    -.6019525    .2334012
         97  |   .0200432   .1372194     0.15   0.884     -.252609    .2926953
         98  |  -.0508596   .1405827    -0.36   0.718    -.3301944    .2284751
         99  |  -.0854021   .1790605    -0.48   0.635    -.4411914    .2703873
        102  |     .45792   .5546551     0.83   0.411    -.6441679    1.560008
        103  |   1.219101   .1909067     6.39   0.000     .8397733    1.598428
        104  |   .7442558   .1660291     4.48   0.000     .4143596    1.074152
        107  |   .1503786   .1425039     1.06   0.294    -.1327736    .4335309
        109  |  -.1515165   .1597917    -0.95   0.346    -.4690191    .1659862
        110  |  -.0250929   .1330267    -0.19   0.851    -.2894142    .2392283
        113  |   -.055325   .1361272    -0.41   0.685     -.325807    .2151569
        114  |   -.126459   .1598433    -0.79   0.431    -.4440643    .1911463
        117  |   -.055325   .1361272    -0.41   0.685     -.325807    .2151569
        118  |  -.1259968   .1353228    -0.93   0.354    -.3948803    .1428867
        137  |  -.1652905   .1442183    -1.15   0.255    -.4518492    .1212682
        138  |  -.0906901   .1396421    -0.65   0.518     -.368156    .1867758
        141  |  -.2477079   .1414445    -1.75   0.083    -.5287552    .0333393
        142  |  -.0644814   .1535571    -0.42   0.676    -.3695961    .2406334
        145  |  -.1377424   .1299979    -1.06   0.292    -.3960454    .1205605
        146  |  -.0554667   .1378125    -0.40   0.688    -.3292971    .2183637
        149  |   .4034663   .4736822     0.85   0.397    -.5377301    1.344663
        150  |   .8972523   .1425511     6.29   0.000     .6140064    1.180498
        153  |   -.055325   .1361272    -0.41   0.685     -.325807    .2151569
        154  |  -.0435605   .1445781    -0.30   0.764    -.3308342    .2437131
        157  |  -.0075982   .1556953    -0.05   0.961    -.3169614     .301765
        158  |  -.0275026   .2893671    -0.10   0.924    -.6024688    .5474636
        159  |   .0606454   .1386928     0.44   0.663    -.2149343    .3362251
        160  |   .7370601   .1989894     3.70   0.000     .3416725    1.132448
        162  |   .7370601   .1989894     3.70   0.000     .3416725    1.132448
        171  |  -.0758418   .1749631    -0.43   0.666    -.4234899    .2718062
        172  |   .2079347   .1630499     1.28   0.206     -.116042    .5319115
        173  |  -.0758418   .1749631    -0.43   0.666    -.4234899    .2718062
        174  |  -.2629399   .1989894    -1.32   0.190    -.6583275    .1324477
        175  |  -.0075982   .1556953    -0.05   0.961    -.3169614     .301765
        176  |  -.2629399   .1989894    -1.32   0.190    -.6583275    .1324477
        177  |   .9506799   .1452279     6.55   0.000     .6621152    1.239245
        178  |   .4724974   .3132964     1.51   0.135    -.1500158    1.095011
        180  |   .4724974   .3132964     1.51   0.135    -.1500158    1.095011
        181  |  -.1595594   .1604724    -0.99   0.323    -.4784146    .1592958
        182  |  -.0220061   .1398009    -0.16   0.875    -.2997876    .2557753
        183  |   .9840958   .1517025     6.49   0.000     .6826662    1.285525
        184  |   .2490914   .4099281     0.61   0.545     -.565427     1.06361
        185  |  -.1824872   .1483023    -1.23   0.222    -.4771606    .1121863
        186  |  -.0266985   .1535692    -0.17   0.862    -.3318373    .2784403
        187  |  -.0159042   .1517025    -0.10   0.917    -.3173338    .2855253
        189  |  -.0159042   .1517025    -0.10   0.917    -.3173338    .2855253
        191  |  -.0159042   .1517025    -0.10   0.917    -.3173338    .2855253
        193  |  -.0159042   .1517025    -0.10   0.917    -.3173338    .2855253
        195  |  -.0762679   .1556152    -0.49   0.625    -.3854719    .2329361
        196  |   .4637627   .4486512     1.03   0.304    -.4276976    1.355223
        197  |   .2583141   .4159269     0.62   0.536    -.5681237    1.084752
        198  |  -.1053901   .1443966    -0.73   0.467    -.3923031    .1815228
        199  |  -.0750192    .138588    -0.54   0.590    -.3503906    .2003522
        200  |  -.0428521   .1376175    -0.31   0.756    -.3162952    .2305909
        201  |   .9008043   .1579698     5.70   0.000     .5869216    1.214687
        202  |   1.004575   .1247925     8.05   0.000     .7566154    1.252535
        203  |   .9840958   .1517025     6.49   0.000     .6826662    1.285525
        204  |  -.0126213   .1380486    -0.09   0.927    -.2869209    .2616782
        205  |  -.0214294    .134681    -0.16   0.874    -.2890376    .2461788
        206  |   .5022762   .4487344     1.12   0.266    -.3893494    1.393902
        207  |   .2582809   .2793077     0.92   0.358    -.2966975    .8132593
        210  |   -.033719   .1423078    -0.24   0.813    -.3164815    .2490436
        211  |  -.1070293   .1393343    -0.77   0.444    -.3838835     .169825
        212  |  -.0551416   .1357609    -0.41   0.686    -.3248956    .2146123
        213  |  -.0687507   .1678827    -0.41   0.683    -.4023301    .2648286
        214  |  -.1437757   .1719441    -0.84   0.405    -.4854248    .1978735
        215  |   .9448584   .1357609     6.96   0.000     .6751044    1.214612
        216  |   .4661898   .4186399     1.11   0.268    -.3656388    1.298018
        217  |   .3013201   .3785853     0.80   0.428    -.4509209    1.053561
        219  |   .3722048   .3997079     0.93   0.354    -.4220063    1.166416
        221  |   .6052012   .3901046     1.55   0.124    -.1699283    1.380331
        227  |  -.0932645   .1515346    -0.62   0.540    -.3943605    .2078316
        231  |  -.0686684   .1452628    -0.47   0.638    -.3573024    .2199656
        233  |  -.0186332   .1347701    -0.14   0.890    -.2864184     .249152
        235  |  -.0895206   .1369538    -0.65   0.515     -.361645    .1826037
        237  |   .9968381   .1527174     6.53   0.000     .6933918    1.300284
        239  |   .9817883   .1384921     7.09   0.000     .7066074    1.256969
        241  |   .0060586   .1330281     0.05   0.964    -.2582653    .2703825
        243  |  -.1908798   .1502684    -1.27   0.207    -.4894599    .1077002
        244  |  -.0736273   .1312894    -0.56   0.576    -.3344965     .187242
        245  |   .0060586   .1330281     0.05   0.964    -.2582653    .2703825
        247  |    .570782   .3123069     1.83   0.071    -.0497652    1.191329
        248  |  -.1043068   .1243206    -0.84   0.404    -.3513292    .1427156
        250  |  -.0608127   .1433364    -0.42   0.672    -.3456191    .2239936
        251  |   .0985136   .3668667     0.27   0.789    -.6304428      .82747
        252  |  -.0765422   .1248299    -0.61   0.541    -.3245765    .1714921
        267  |  -.0644721   .1342766    -0.48   0.632    -.3312769    .2023326
        269  |   .0060586   .1330281     0.05   0.964    -.2582653    .2703825
        271  |  -.3199935   .1509508    -2.12   0.037    -.6199294   -.0200576
        272  |  -.1251486   .1477074    -0.85   0.399    -.4186401    .1683429
        274  |  -.0608127   .1433364    -0.42   0.672    -.3456191    .2239936
        275  |  -.1819605   .1968806    -0.92   0.358    -.5731581    .2092372
        276  |  -.0736273   .1312894    -0.56   0.576    -.3344965     .187242
        278  |  -.0608127   .1433364    -0.42   0.672    -.3456191    .2239936
        279  |  -.1021007   .1571846    -0.65   0.518    -.4144232    .2102218
        280  |  -.0765422   .1248299    -0.61   0.541    -.3245765    .1714921
        283  |   .0078647    .142697     0.06   0.956    -.2756712    .2914006
        284  |   .8134923    .139021     5.85   0.000     .5372606    1.089724
        285  |  -.1039069   .1455941    -0.71   0.477    -.3931993    .1853855
        287  |  -.0644721   .1342766    -0.48   0.632    -.3312769    .2023326
             |
      fYes_T |   .1099655   .0600658     1.83   0.070     -.009384     .229315
        mage |   -.003476   .0030244    -1.15   0.254    -.0094855    .0025335
    mmarried |   .0832591   .0713765     1.17   0.247    -.0585645    .2250828
       makan |  -.0507722   .0629428    -0.81   0.422    -.1758383    .0742939
mselfemplo~d |  -.1194464   .0515361    -2.32   0.023    -.2218476   -.0170453
       m2q1a |   .0045736   .0159829     0.29   0.775    -.0271842    .0363313
        trt2 |  -.0657605   .0914996    -0.72   0.474    -.2475683    .1160473
             |
      c.trt2#|
    c.bundle |  -.5738896   .2335622    -2.46   0.016    -1.037973   -.1098065
             |
        trt3 |    -.13645   .0983123    -1.39   0.169    -.3317945    .0588945
             |
      c.trt3#|
    c.bundle |  -.2939981   .1885351    -1.56   0.122    -.6686133    .0806172
             |
        trt4 |  -.1355548   .0985797    -1.38   0.173    -.3314306     .060321
             |
      c.trt4#|
    c.bundle |  -.3814404   .1782519    -2.14   0.035    -.7356232   -.0272576
             |
      bundle |   .2505117   .1683858     1.49   0.140    -.0840673    .5850907
       _cons |    .263876   .1532242     1.72   0.089    -.0405772    .5683293
------------------------------------------------------------------------------

. reg fdamt i.distXtrXdateFes fYes_T mage mmarried makan mselfemployed m2q1a  
> trt2 c.trt2#c.bundle trt3 c.trt3#c.bundle trt4 c.trt4#c.bundle bundle, r clu
> ster(ge02) level(95)

Linear regression                               Number of obs     =        332
                                                F(79, 89)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6749
                                                Root MSE          =     .98722

                                  (Std. err. adjusted for 90 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
       fdamt | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
distXtrXda~s |
          4  |  -.0027994   .4930993    -0.01   0.995    -.9825772    .9769784
          5  |   2.050381   1.555049     1.32   0.191    -1.039469    5.140231
          6  |  -.2256524   .5542174    -0.41   0.685    -1.326871    .8755658
          7  |  -.0027994   .4930993    -0.01   0.995    -.9825772    .9769784
          8  |   .1101527    .379166     0.29   0.772     -.643242    .8635475
         11  |   .0211129   .4271824     0.05   0.961    -.8276894    .8699152
         14  |   .0211129   .4271824     0.05   0.961    -.8276894    .8699152
         18  |  -.2256524   .5542174    -0.41   0.685    -1.326871    .8755658
         22  |  -.0027994   .4930993    -0.01   0.995    -.9825772    .9769784
         23  |   .1101563   .4728779     0.23   0.816     -.829442    1.049755
         24  |  -.2256524   .5542174    -0.41   0.685    -1.326871    .8755658
         26  |  -.2107596   .5057095    -0.42   0.678    -1.215594    .7940744
         27  |  -.2256524   .5542174    -0.41   0.685    -1.326871    .8755658
         29  |  -.4637869    .504431    -0.92   0.360    -1.466081    .5385067
         30  |  -.2256524   .5542174    -0.41   0.685    -1.326871    .8755658
         32  |   .0572679   .4203754     0.14   0.892     -.778009    .8925447
         33  |  -.2256524   .5542174    -0.41   0.685    -1.326871    .8755658
         34  |  -.0027994   .4930993    -0.01   0.995    -.9825772    .9769784
         35  |  -.1844663   .3608493    -0.51   0.610    -.9014661    .5325335
         37  |  -.2118655   .4207489    -0.50   0.616    -1.047885    .6241535
         38  |   .4536884   .8437022     0.54   0.592     -1.22273    2.130107
         39  |  -.1819403   .4143251    -0.44   0.662    -1.005195    .6413148
         40  |   1.082475   .9410479     1.15   0.253    -.7873667    2.952317
         41  |  -.1343033   .4431476    -0.30   0.763    -1.014828    .7462216
         42  |   .0428559   .5464944     0.08   0.938    -1.043017    1.128729
         51  |  -.2407616    .406502    -0.59   0.555    -1.048472    .5669493
         52  |   -.552947   .5125623    -1.08   0.284    -1.571398    .4655034
         53  |  -.3341756     .41532    -0.80   0.423    -1.159408    .4910563
         54  |   .0528389   .6437326     0.08   0.935    -1.226244    1.331922
         55  |   -.109916   .4297438    -0.26   0.799    -.9638077    .7439757
         56  |   .1860774   .5334825     0.35   0.728    -.8739411    1.246096
         57  |   1.355882    1.04966     1.29   0.200    -.7297699    3.441534
         58  |   .5813313   .5978476     0.97   0.334    -.6065791    1.769242
         61  |  -.1897351   .4896464    -0.39   0.699    -1.162652    .7831819
         62  |  -.1591078   .4912362    -0.32   0.747    -1.135184    .8169681
         63  |  -.0149164   .5518237    -0.03   0.978    -1.111378    1.081546
         64  |  -.4302432    .498518    -0.86   0.390    -1.420788    .5603014
         65  |    .087061    .595927     0.15   0.884    -1.097033    1.271155
         66  |   3.913682   .5192086     7.54   0.000     2.882026    4.945339
         67  |   2.086587   .9781339     2.13   0.036     .1430556    4.030118
         68  |  -.4302432    .498518    -0.86   0.390    -1.420788    .5603014
         69  |   .3719805   .4833797     0.77   0.444    -.5884846    1.332446
         71  |   .4892214   .4970265     0.98   0.328    -.4983596    1.476802
         72  |  -.4302432    .498518    -0.86   0.390    -1.420788    .5603014
         89  |   .2583532   .5081889     0.51   0.612    -.7514073    1.268114
         90  |   .0850921    .488201     0.17   0.862    -.8849529    1.055137
         91  |   -.288127   .6879727    -0.42   0.676    -1.655114     1.07886
         93  |   .2709867   .4536422     0.60   0.552    -.6303907    1.172364
         94  |  -.2861754   .5179716    -0.55   0.582    -1.315374    .7430233
         95  |  -.5883532   .6255624    -0.94   0.349    -1.831332     .654626
         97  |   -.041673   .4364467    -0.10   0.924    -.9088832    .8255373
         98  |  -.1591078   .4912362    -0.32   0.747    -1.135184    .8169681
         99  |  -.3094343    .552471    -0.56   0.577    -1.407182    .7883138
        102  |     .36934   .7677438     0.48   0.632    -1.156151    1.894831
        103  |   1.535346   .4975206     3.09   0.003     .5467827    2.523908
        104  |   .1700415   .5451493     0.31   0.756    -.9131586    1.253242
        107  |   .3376503   .4472575     0.75   0.452    -.5510409    1.226342
        109  |  -.5417043   .5459206    -0.99   0.324    -1.626437    .5430283
        110  |  -.1254038   .4355005    -0.29   0.774     -.990734    .7399263
        113  |  -.2087911   .4604175    -0.45   0.651    -1.123631    .7060487
        114  |  -.4042215   .5045307    -0.80   0.425    -1.406713    .5982702
        117  |  -.2087911   .4604175    -0.45   0.651    -1.123631    .7060487
        118  |  -.3854783   .4658024    -0.83   0.410    -1.311018    .5400612
        137  |  -.6085063   .4762319    -1.28   0.205    -1.554769    .3377562
        138  |  -.3169689   .4761723    -0.67   0.507    -1.263113    .6291753
        141  |  -.8746175   .4712867    -1.86   0.067    -1.811054    .0618191
        142  |  -.1154533   .5047661    -0.23   0.820    -1.118413    .8875062
        145  |  -.4749022   .4286292    -1.11   0.271    -1.326579    .3767749
        146  |  -.2066202   .4534646    -0.46   0.650    -1.107645    .6944043
        149  |   .6581534   .9633754     0.68   0.496    -1.256053     2.57236
        150  |    .660922   .4638788     1.42   0.158    -.2607953    1.582639
        153  |  -.2087911   .4604175    -0.45   0.651    -1.123631    .7060487
        154  |  -.1937064    .478717    -0.40   0.687    -1.144907    .7574942
        157  |  -.0059328   .5078373    -0.01   0.991    -1.014995    1.003129
        158  |  -.0401448   .7879509    -0.05   0.959    -1.605786    1.525497
        159  |   .1922065   .4789249     0.40   0.689     -.759407     1.14382
        160  |    3.34759   .5236228     6.39   0.000     2.307163    4.388017
        162  |   .3475901   .5236228     0.66   0.509    -.6928372    1.388017
        171  |   -.204072   .5951544    -0.34   0.732    -1.386631    .9784871
        172  |   .5721202   .5195558     1.10   0.274     -.460226    1.604467
        173  |   -.204072   .5951544    -0.34   0.732    -1.386631    .9784871
        174  |  -.6524099   .5236228    -1.25   0.216    -1.692837    .3880175
        175  |  -.0059328   .5078373    -0.01   0.991    -1.014995    1.003129
        176  |  -.6524099   .5236228    -1.25   0.216    -1.692837    .3880175
        177  |   .7924913   .4942979     1.60   0.112    -.1896681    1.774651
        178  |   .4598552   .5119746     0.90   0.372    -.5574274    1.477138
        180  |   1.459855   1.014919     1.44   0.154    -.5567672    3.476478
        181  |  -.5373346    .515109    -1.04   0.300    -1.560845    .4861759
        182  |   -.137806   .4721013    -0.29   0.771    -1.075861    .8002492
        183  |   4.012332   .4860296     8.26   0.000     3.046601    4.978062
        184  |   .9738297   1.610634     0.60   0.547    -2.226465    4.174124
        185  |  -.6954769   .4948189    -1.41   0.163    -1.678672    .2877177
        186  |  -.1566907   .5213053    -0.30   0.764    -1.192513    .8791318
        187  |   .0123317   .4860296     0.03   0.980    -.9533988    .9780622
        189  |   .0123317   .4860296     0.03   0.980    -.9533988    .9780622
        191  |   .0123317   .4860296     0.03   0.980    -.9533988    .9780622
        193  |   .0123317   .4860296     0.03   0.980    -.9533988    .9780622
        195  |  -.1834303   .5052064    -0.36   0.717    -1.187265     .820404
        196  |   2.318115   2.298575     1.01   0.316    -2.249104    6.885334
        197  |   .1124592   .5763842     0.20   0.846    -1.032804    1.257722
        198  |  -.4127812   .4751441    -0.87   0.387    -1.356882      .53132
        199  |  -.2208741   .4617563    -0.48   0.634    -1.138374    .6966258
        200  |  -.2065498    .466345    -0.44   0.659    -1.133167    .7200677
        201  |   2.658427   .5672126     4.69   0.000     1.531388    3.785467
        202  |   4.986934   .4288598    11.63   0.000     4.134799    5.839069
        203  |   5.012332   .4860296    10.31   0.000     4.046601    5.978062
        204  |  -.1000366   .4715304    -0.21   0.832    -1.036957    .8368842
        205  |  -.0891077   .4508215    -0.20   0.844    -.9848803    .8066649
        206  |   1.938804   1.799567     1.08   0.284    -1.636897    5.514505
        207  |   1.137498   1.408375     0.81   0.421    -1.660914    3.935911
        210  |  -.1371035   .4840793    -0.28   0.778    -1.098959    .8247517
        211  |  -.4035804    .483753    -0.83   0.406    -1.364787    .5576265
        212  |    -.24379   .4527865    -0.54   0.592    -1.143467    .6558871
        213  |  -.2818511   .5672931    -0.50   0.621     -1.40905    .8453482
        214  |  -.5608759   .5670673    -0.99   0.325    -1.687627    .5658747
        215  |     .75621   .4527865     1.67   0.098    -.1434671    1.655887
        216  |   1.338839   1.249139     1.07   0.287    -1.143174    3.820852
        217  |    1.48862   1.876887     0.79   0.430    -2.240714    5.217954
        219  |   1.505529   1.558603     0.97   0.337    -1.591382    4.602441
        221  |   .4330179   .5726818     0.76   0.452    -.7048885    1.570924
        227  |  -.3831816   .5302471    -0.72   0.472    -1.436771    .6704081
        231  |  -.3112853   .5140656    -0.61   0.546    -1.332723    .7101522
        233  |  -.1380003    .452688    -0.30   0.761    -1.037482    .7614812
        235  |  -.3789622   .4531004    -0.84   0.405    -1.279263    .5213388
        237  |   1.907087   .9738336     1.96   0.053    -.0278995    3.842074
        239  |   5.876746   1.221642     4.81   0.000     3.449369    8.304123
        241  |  -.0545598    .449224    -0.12   0.904    -.9471584    .8380387
        243  |  -.5476338   .5118781    -1.07   0.288    -1.564725    .4694571
        244  |  -.2472733   .4447482    -0.56   0.580    -1.130978    .6364319
        245  |  -.0545598    .449224    -0.12   0.904    -.9471584    .8380387
        247  |   2.402459   1.332857     1.80   0.075    -.2459001    5.050818
        248  |  -.3599589    .417101    -0.86   0.390     -1.18873    .4688118
        250  |  -.2844465   .4667256    -0.61   0.544     -1.21182    .6429273
        251  |  -.3685131   .5333078    -0.69   0.491    -1.428184    .6911582
        252  |  -.2856721   .4224712    -0.68   0.501    -1.125113    .5537693
        267  |  -.2598419    .453917    -0.57   0.568    -1.161765    .6420815
        269  |  -.0545598    .449224    -0.12   0.904    -.9471584    .8380387
        271  |  -.9228488   .4945505    -1.87   0.065     -1.90551    .0598125
        272  |  -.4600162   .4822425    -0.95   0.343    -1.418222    .4981894
        274  |  -.2844465   .4667256    -0.61   0.544     -1.21182    .6429273
        275  |  -.5143509   .6170998    -0.83   0.407    -1.740515    .7118132
        276  |  -.2472733   .4447482    -0.56   0.580    -1.130978    .6364319
        278  |  -.2844465   .4667256    -0.61   0.544     -1.21182    .6429273
        279  |  -.3991025   .5287501    -0.75   0.452    -1.449718    .6515127
        280  |  -.2856721   .4224712    -0.68   0.501    -1.125113    .5537693
        283  |   .0006128   .4897444     0.00   0.999     -.972499    .9737245
        284  |   .3146127    .457154     0.69   0.493    -.5937426    1.222968
        285  |  -.4542751   .4715535    -0.96   0.338    -1.391242    .4826916
        287  |  -.2598419    .453917    -0.57   0.568    -1.161765    .6420815
             |
      fYes_T |   .3997153   .1878435     2.13   0.036     .0264743    .7729562
        mage |  -.0103943   .0110127    -0.94   0.348    -.0322763    .0114876
    mmarried |   .2917687    .262395     1.11   0.269    -.2296046    .8131421
       makan |  -.1580471   .2053206    -0.77   0.443    -.5660148    .2499205
mselfemplo~d |  -.4113919   .2028878    -2.03   0.046    -.8145256   -.0082582
       m2q1a |   .0255254   .0450736     0.57   0.573    -.0640349    .1150858
        trt2 |  -.1341768   .2968698    -0.45   0.652    -.7240507    .4556971
             |
      c.trt2#|
    c.bundle |  -1.442176    .642908    -2.24   0.027     -2.71962   -.1647311
             |
        trt3 |  -.3755999   .3111025    -1.21   0.231    -.9937539    .2425541
             |
      c.trt3#|
    c.bundle |  -.7102356    .469674    -1.51   0.134    -1.643468    .2229967
             |
        trt4 |  -.3388428   .3098936    -1.09   0.277    -.9545948    .2769093
             |
      c.trt4#|
    c.bundle |  -1.078249   .4547125    -2.37   0.020    -1.981753   -.1747454
             |
      bundle |   .6751359   .3933443     1.72   0.090    -.1064308    1.456703
       _cons |   .7659149   .5425627     1.41   0.162    -.3121457    1.843975
------------------------------------------------------------------------------

. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
end of do-file

. do "$do_loc/Revenues_Mar.19.2023.do" // 3-ish minutes?

. /*
> JPE2023-Annan
> y = revenues: momo + non-momo*
> Phone Surveys + Intensive Tracking: April 2020+
> 
> Input:
>         - FFPhone in 2020/MerchantsData.dta
>         - data-Mgt/Stats?/Mkt_census_xtics_+_interventions_localized.dta
> Output:
>         -[regressions]
> */
. 
. use "$dta_loc_repl/02_final/Merchants_+_Mktcensus_+_Interventions.dta", clea
> r

. 
. ** Table B.7 ---------------------------------------------------------------
> ----
. *Attrition - Test for Significance by Treatment Program
. sum dropouts if trt_pool==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    dropouts |         32      .21875    .4200134          0          1

. reg dropouts trt_pool, r

Linear regression                               Number of obs     =        130
                                                F(1, 128)         =       0.45
                                                Prob > F          =     0.5035
                                                R-squared         =     0.0039
                                                Root MSE          =     .38382

------------------------------------------------------------------------------
             |               Robust
    dropouts | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    trt_pool |  -.0554847    .082703    -0.67   0.503    -.2191266    .1081573
       _cons |     .21875    .073648     2.97   0.004     .0730249    .3644751
------------------------------------------------------------------------------

. reg dropouts i.trt, r

Linear regression                               Number of obs     =        130
                                                F(3, 126)         =       0.40
                                                Prob > F          =     0.7502
                                                R-squared         =     0.0089
                                                Root MSE          =     .38588

------------------------------------------------------------------------------
             |               Robust
    dropouts | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         trt |
          1  |  -.0574597   .1000619    -0.57   0.567    -.2554792    .1405599
          2  |    -.09375    .095061    -0.99   0.326    -.2818729    .0943729
          3  |    -.01875    .101127    -0.19   0.853    -.2188774    .1813774
             |
       _cons |     .21875   .0742302     2.95   0.004     .0718507    .3656493
------------------------------------------------------------------------------

. 
. 
. ** Figure B.5 --------------------------------------------------------------
> ----
. distplot v0a //customers answer quicker than vendors/business (as expected)

. hist v0a, gap(10) percent xtitle("Vendors: Number of phone call times before
>  answering survey")
(bin=10, start=1, width=.6)

. gr export "$output_loc/main_results/vendor_calltimeS.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/main_results/vendor_calltimeS.eps saved as EPS format

. 
. gen districtID = ge01

. 
. **control means?
. sum mmtotamt_cust_t1 bus_exit nonmmtotamt_cust_t1 totamt_cust_t1 if trtment=
> =0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
mmtotamt_c~1 |         25      792.76     669.532          0       2222
    bus_exit |         32      .21875    .4200134          0          1
nonmmtotam~1 |         25      239.48    450.6298          0       2222
totamt_cus~1 |         25     1032.24    768.7026          0       2963

. 
. 
. ** Table 6 -----------------------------------------------------------------
> ----
. regress mmtotamt_cust_t1 mmtotamt_cust_t0 i.districtID mage mmarried makan m
> selfemployed m2q1a i.m3q1 trtment, r

Linear regression                               Number of obs     =        107
                                                F(15, 91)         =       4.89
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3219
                                                Root MSE          =     827.94

------------------------------------------------------------------------------
             |               Robust
mmtotamt_c~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
mmtotamt_c~0 |   .0090737   .0130858     0.69   0.490    -.0169198    .0350671
             |
  districtID |
          3  |    283.603   345.9106     0.82   0.414    -403.5059    970.7119
          4  |  -119.9951   230.3445    -0.52   0.604    -577.5462    337.5559
          5  |   802.4011   253.5242     3.16   0.002     298.8064    1305.996
          6  |   135.6977   432.8211     0.31   0.755    -724.0483    995.4437
          7  |   1023.136   472.6141     2.16   0.033     84.34595    1961.925
          8  |   877.9332   287.7446     3.05   0.003      306.364    1449.502
          9  |  -28.78967   258.9183    -0.11   0.912    -543.0991    485.5198
             |
        mage |   12.79577    15.1114     0.85   0.399    -17.22117    42.81271
    mmarried |   127.7077   246.3656     0.52   0.605    -361.6674    617.0827
       makan |   18.26443    210.453     0.09   0.931    -399.7746    436.3035
mselfemplo~d |   132.0561   198.7001     0.66   0.508    -262.6371    526.7494
       m2q1a |  -28.82207   47.71181    -0.60   0.547    -123.5957    65.95158
      2.m3q1 |  -196.2415   168.0294    -1.17   0.246    -530.0113    137.5284
     trtment |   436.6255   178.4692     2.45   0.016     82.11837    791.1327
       _cons |   81.32199   466.2873     0.17   0.862    -844.9005    1007.545
------------------------------------------------------------------------------

. regress bus_exit i.districtID mage mmarried makan mselfemployed m2q1a i.m3q1
>  trtment, r

Linear regression                               Number of obs     =        129
                                                F(15, 113)        =     245.00
                                                Prob > F          =     0.0000
                                                R-squared         =     0.5634
                                                Root MSE          =     .26551

------------------------------------------------------------------------------
             |               Robust
    bus_exit | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |   -.888054   .1277835    -6.95   0.000    -1.141216   -.6348917
          3  |  -.7075964   .1464257    -4.83   0.000    -.9976922   -.4175006
          4  |  -.9744862   .0742349   -13.13   0.000    -1.121559   -.8274135
          5  |  -1.027481   .0449185   -22.87   0.000    -1.116473   -.9384898
          6  |  -1.008338   .0453413   -22.24   0.000    -1.098168    -.918509
          7  |  -.8882956   .1219901    -7.28   0.000     -1.12998   -.6466112
          8  |  -.9993148    .031583   -31.64   0.000    -1.061887   -.9367432
          9  |  -.8785834    .081038   -10.84   0.000    -1.039134   -.7180326
             |
        mage |  -.0041058   .0067582    -0.61   0.545     -.017495    .0092833
    mmarried |   .0317779   .0925833     0.34   0.732    -.1516463    .2152022
       makan |   .0131218   .0524002     0.25   0.803    -.0906925     .116936
mselfemplo~d |   .0279765   .0589801     0.47   0.636    -.0888737    .1448266
       m2q1a |  -.0028854   .0095305    -0.30   0.763    -.0217671    .0159963
      2.m3q1 |   .0343929   .0648426     0.53   0.597    -.0940719    .1628578
     trtment |  -.0690033   .0585168    -1.18   0.241    -.1849357    .0469292
       _cons |   1.143297   .1815037     6.30   0.000     .7837051    1.502888
------------------------------------------------------------------------------

. 
. tab trt, gen(trt)

        trt |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         32       24.62       24.62
          1 |         31       23.85       48.46
          2 |         32       24.62       73.08
          3 |         35       26.92      100.00
------------+-----------------------------------
      Total |        130      100.00

. regress mmtotamt_cust_t1 mmtotamt_cust_t0 i.districtID mage mmarried makan m
> selfemployed m2q1a i.m3q1 trt2 trt3 trt4, r

Linear regression                               Number of obs     =        107
                                                F(17, 89)         =       4.89
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3258
                                                Root MSE          =     834.78

------------------------------------------------------------------------------
             |               Robust
mmtotamt_c~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
mmtotamt_c~0 |   .0089991   .0136186     0.66   0.510    -.0180609     .036059
             |
  districtID |
          3  |   304.2369   351.7603     0.86   0.389    -394.7032    1003.177
          4  |  -117.8598   244.2393    -0.48   0.631    -603.1582    367.4386
          5  |   808.8636   271.2873     2.98   0.004     269.8215    1347.906
          6  |   147.2044   434.0726     0.34   0.735    -715.2885    1009.697
          7  |   1027.456   483.7044     2.12   0.036     66.34578    1988.567
          8  |   893.3885   302.6474     2.95   0.004     292.0346    1494.742
          9  |  -18.77056    270.872    -0.07   0.945    -556.9875    519.4464
             |
        mage |   13.43771     15.658     0.86   0.393     -17.6744    44.54981
    mmarried |   107.6609   254.8499     0.42   0.674    -398.7204    614.0422
       makan |   4.335822   208.1801     0.02   0.983    -409.3135    417.9852
mselfemplo~d |   128.8225   202.6518     0.64   0.527    -273.8423    531.4873
       m2q1a |   -27.7618   49.09302    -0.57   0.573    -125.3086    69.78499
      2.m3q1 |  -173.8562   174.0911    -1.00   0.321    -519.7716    172.0592
        trt2 |   523.6426   222.0469     2.36   0.021     82.44013     964.845
        trt3 |   418.4564   259.8872     1.61   0.111     -97.9339    934.8466
        trt4 |   358.1305   198.1316     1.81   0.074    -35.55279    751.8137
       _cons |   65.63253    484.323     0.14   0.893    -896.7069    1027.972
------------------------------------------------------------------------------

. test _b[trt2]=_b[trt4]

 ( 1)  trt2 - trt4 = 0

       F(  1,    89) =    0.61
            Prob > F =    0.4363

. test _b[trt3]=_b[trt4]

 ( 1)  trt3 - trt4 = 0

       F(  1,    89) =    0.06
            Prob > F =    0.8103

. test _b[trt2]=_b[trt3]

 ( 1)  trt2 - trt3 = 0

       F(  1,    89) =    0.17
            Prob > F =    0.6801

. test _b[trt2] + _b[trt3] =_b[trt4]

 ( 1)  trt2 + trt3 - trt4 = 0

       F(  1,    89) =    2.83
            Prob > F =    0.0961

. regress bus_exit i.districtID mage mmarried makan mselfemployed m2q1a i.m3q1
>  trt2 trt3 trt4, r

Linear regression                               Number of obs     =        129
                                                F(17, 111)        =     110.07
                                                Prob > F          =     0.0000
                                                R-squared         =     0.5711
                                                Root MSE          =     .26555

------------------------------------------------------------------------------
             |               Robust
    bus_exit | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |   -.886977   .1243956    -7.13   0.000    -1.133475   -.6404788
          3  |  -.7110293   .1452536    -4.90   0.000     -.998859   -.4231995
          4  |  -.9725987   .0754712   -12.89   0.000     -1.12215   -.8230475
          5  |  -1.032394   .0497195   -20.76   0.000    -1.130916   -.9338711
          6  |  -1.011908   .0500678   -20.21   0.000     -1.11112   -.9126951
          7  |   -.893687   .1184032    -7.55   0.000    -1.128311   -.6590631
          8  |  -1.000709   .0363911   -27.50   0.000    -1.072821    -.928598
          9  |  -.8759238   .0846662   -10.35   0.000    -1.043695   -.7081521
             |
        mage |   -.003868   .0068815    -0.56   0.575    -.0175041    .0097681
    mmarried |   .0299913   .0934551     0.32   0.749    -.1551961    .2151787
       makan |   .0126816    .053343     0.24   0.813    -.0930211    .1183843
mselfemplo~d |   .0328368   .0580491     0.57   0.573    -.0821914    .1478651
       m2q1a |  -.0032427   .0093909    -0.35   0.731    -.0218514     .015366
      2.m3q1 |   .0280677   .0671684     0.42   0.677     -.105031    .1611663
        trt2 |  -.1000416   .0608674    -1.64   0.103    -.2206543    .0205712
        trt3 |   -.094358   .0638177    -1.48   0.142    -.2208171     .032101
        trt4 |   -.017767   .0760564    -0.23   0.816    -.1684779    .1329439
       _cons |    1.13774   .1854703     6.13   0.000     .7702182    1.505262
------------------------------------------------------------------------------

. test _b[trt2]=_b[trt4]

 ( 1)  trt2 - trt4 = 0

       F(  1,   111) =    1.66
            Prob > F =    0.2007

. test _b[trt3]=_b[trt4]

 ( 1)  trt3 - trt4 = 0

       F(  1,   111) =    1.56
            Prob > F =    0.2137

. test _b[trt2]=_b[trt3]

 ( 1)  trt2 - trt3 = 0

       F(  1,   111) =    0.02
            Prob > F =    0.8881

. test _b[trt2] + _b[trt3] =_b[trt4]

 ( 1)  trt2 + trt3 - trt4 = 0

       F(  1,   111) =    3.80
            Prob > F =    0.0537

. 
. 
. 
. 
. 
. ** Table 8 -----------------------------------------------------------------
> ----
. *SPILLOVERS - non momo sales -- MAIN TEXT
. *bundling w non momo?
. tab m3q1 //75-79% of sample bundled stores

  Currently |
     do you |
offer other |
services at |
       your |
   business |
   center , |
 other than |
      M-Mon |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        102       79.07       79.07
          2 |         27       20.93      100.00
------------+-----------------------------------
      Total |        129      100.00

. tab m3q1 if dropouts==0 

  Currently |
     do you |
offer other |
services at |
       your |
   business |
   center , |
 other than |
      M-Mon |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         85       79.44       79.44
          2 |         22       20.56      100.00
------------+-----------------------------------
      Total |        107      100.00

. *we code non momo sales to 0 for momo only stores, to prevent n changing acr
> oss specs*
. replace nonmmtotamt_cust_t1=0 if m3q1==2 & dropouts==0
(22 real changes made)

. replace nonmmtotamt_cust_t0=0 if m3q1==2 & dropouts==0
(22 real changes made)

. replace totamt_cust_t1=0 if m3q1==2 & dropouts==0
(22 real changes made)

. replace totamt_cust_t0=0 if m3q1==2 & dropouts==0
(22 real changes made)

. 
. regress nonmmtotamt_cust_t1 nonmmtotamt_cust_t0 i.districtID mage mmarried m
> akan mselfemployed m2q1a i.m3q1 trtment, r

Linear regression                               Number of obs     =        107
                                                F(15, 91)         =       3.98
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3964
                                                Root MSE          =      256.7

------------------------------------------------------------------------------
             |               Robust
nonmmtotam~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
nonmmtotam~0 |  -.0001168    .098334    -0.00   0.999    -.1954451    .1952115
             |
  districtID |
          3  |   49.33856    154.179     0.32   0.750    -256.9192    355.5963
          4  |   89.37374   61.26199     1.46   0.148    -32.31568    211.0632
          5  |   93.74165   97.60702     0.96   0.339    -100.1427     287.626
          6  |   559.6679   178.5739     3.13   0.002     204.9529    914.3829
          7  |   246.5865   101.9699     2.42   0.018     44.03582    449.1372
          8  |   111.7278   54.64484     2.04   0.044     3.182535    220.2731
          9  |   7.582423   50.34301     0.15   0.881    -92.41779    107.5826
             |
        mage |  -.6701764   3.652452    -0.18   0.855    -7.925324    6.584971
    mmarried |    26.4209   56.73534     0.47   0.643    -86.27689    139.1187
       makan |   64.14806     56.694     1.13   0.261     -48.4676    176.7637
mselfemplo~d |  -45.46799   53.95695    -0.84   0.402    -152.6469    61.71087
       m2q1a |   23.88877   20.73512     1.15   0.252      -17.299    65.07654
      2.m3q1 |  -214.1722   46.83067    -4.57   0.000    -307.1955   -121.1488
     trtment |   132.7775   58.69281     2.26   0.026      16.1915    249.3636
       _cons |  -24.62476    113.373    -0.22   0.829    -249.8263    200.5768
------------------------------------------------------------------------------

. regress totamt_cust_t1 totamt_cust_t0 i.districtID mage mmarried makan mself
> employed m2q1a i.m3q1 trtment, r

Linear regression                               Number of obs     =        107
                                                F(15, 91)         =      12.04
                                                Prob > F          =     0.0000
                                                R-squared         =     0.4504
                                                Root MSE          =     908.07

------------------------------------------------------------------------------
             |               Robust
totamt_cus~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
totamt_cus~0 |   .0181485   .0140112     1.30   0.198    -.0096829      .04598
             |
  districtID |
          3  |   247.6569    412.581     0.60   0.550    -571.8845    1067.198
          4  |  -66.11714   204.0018    -0.32   0.747    -471.3416    339.1073
          5  |   633.8501   298.1353     2.13   0.036     41.64098    1226.059
          6  |   644.2233   511.7403     1.26   0.211    -372.2859    1660.733
          7  |   1175.496   507.6937     2.32   0.023     167.0246    2183.967
          8  |   824.1177   267.0767     3.09   0.003     293.6026    1354.633
          9  |  -99.91784    223.885    -0.45   0.656    -544.6379    344.8022
             |
        mage |   13.45511   14.82702     0.91   0.367    -15.99694    42.90717
    mmarried |   57.80315   246.2005     0.23   0.815    -431.2438    546.8501
       makan |   115.9029   233.3411     0.50   0.621    -347.6006    579.4064
mselfemplo~d |   41.56203   197.9912     0.21   0.834    -351.7232    434.8472
       m2q1a |  -10.09182   52.21125    -0.19   0.847    -113.8031    93.61942
      2.m3q1 |  -1168.033   149.2524    -7.83   0.000    -1464.504   -871.5611
     trtment |   537.6268   195.8947     2.74   0.007     148.5061    926.7475
       _cons |   150.5395   461.0437     0.33   0.745    -765.2672    1066.346
------------------------------------------------------------------------------

. 
. 
. regress nonmmtotamt_cust_t1 nonmmtotamt_cust_t0 i.districtID mage mmarried m
> akan mselfemployed m2q1a i.m3q1 trt2 trt3 trt4, r

Linear regression                               Number of obs     =        107
                                                F(17, 89)         =       3.41
                                                Prob > F          =     0.0001
                                                R-squared         =     0.4064
                                                Root MSE          =     257.43

------------------------------------------------------------------------------
             |               Robust
nonmmtotam~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
nonmmtotam~0 |   .0169747     .10317     0.16   0.870    -.1880219    .2219714
             |
  districtID |
          3  |   57.87904   155.8047     0.37   0.711    -251.7017    367.4597
          4  |   84.67787   69.07594     1.23   0.223    -52.57455    221.9303
          5  |   84.74084   98.11872     0.86   0.390     -110.219    279.7007
          6  |   553.3832   177.6757     3.11   0.002     200.3454     906.421
          7  |   240.9122   105.9287     2.27   0.025     30.43403    451.3903
          8  |    111.522   60.89967     1.83   0.070    -9.484317    232.5284
          9  |   1.963584   56.22637     0.03   0.972     -109.757    113.6842
             |
        mage |   .0234265   3.754596     0.01   0.995    -7.436875    7.483728
    mmarried |   9.354933     53.661     0.17   0.862    -97.26832    115.9782
       makan |   54.31131    56.3624     0.96   0.338    -57.67957    166.3022
mselfemplo~d |  -48.35187   53.60412    -0.90   0.369    -154.8621    58.15838
       m2q1a |   23.12076   20.31616     1.14   0.258    -17.24701    63.48853
      2.m3q1 |  -196.9389   52.03943    -3.78   0.000    -300.3401   -93.53761
        trt2 |    167.175   73.40903     2.28   0.025     21.31285    313.0372
        trt3 |   80.51831   65.86129     1.22   0.225    -50.34668    211.3833
        trt4 |   141.4084   76.43945     1.85   0.068    -10.47517     293.292
       _cons |  -31.11423   118.1101    -0.26   0.793    -265.7965     203.568
------------------------------------------------------------------------------

. test _b[trt2]=_b[trt4]

 ( 1)  trt2 - trt4 = 0

       F(  1,    89) =    0.10
            Prob > F =    0.7482

. test _b[trt3]=_b[trt4]

 ( 1)  trt3 - trt4 = 0

       F(  1,    89) =    0.96
            Prob > F =    0.3301

. test _b[trt2]=_b[trt3]

 ( 1)  trt2 - trt3 = 0

       F(  1,    89) =    1.51
            Prob > F =    0.2230

. test _b[trt2] + _b[trt3] =_b[trt4]

 ( 1)  trt2 + trt3 - trt4 = 0

       F(  1,    89) =    1.23
            Prob > F =    0.2700

. 
. regress totamt_cust_t1 totamt_cust_t0 i.districtID mage mmarried makan mself
> employed m2q1a i.m3q1 trt2 trt3 trt4, r

Linear regression                               Number of obs     =        107
                                                F(17, 89)         =      11.40
                                                Prob > F          =     0.0000
                                                R-squared         =     0.4622
                                                Root MSE          =     908.31

------------------------------------------------------------------------------
             |               Robust
totamt_cus~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
totamt_cus~0 |   .0182059   .0145446     1.25   0.214    -.0106938    .0471057
             |
  districtID |
          3  |   297.6754   412.9166     0.72   0.473    -522.7811    1118.132
          4  |  -67.50975   232.3543    -0.29   0.772    -529.1928    394.1733
          5  |   633.7176   322.5525     1.96   0.053    -7.187371    1274.623
          6  |   657.1109   502.2562     1.31   0.194    -340.8614    1655.083
          7  |   1176.759   518.9353     2.27   0.026     145.6452    2207.872
          8  |   851.3329   288.1095     2.95   0.004     278.8654      1423.8
          9  |  -87.66202   238.8683    -0.37   0.714    -562.2882    386.9642
             |
        mage |   15.51271   15.07943     1.03   0.306     -14.4498    45.47523
    mmarried |   1.701359   256.1286     0.01   0.995    -507.2208    510.6235
       makan |   80.10741   228.5388     0.35   0.727    -373.9943    534.2091
mselfemplo~d |   33.74764   201.2889     0.17   0.867    -366.2091    433.7044
       m2q1a |  -8.921722   54.57742    -0.16   0.871    -117.3659    99.52245
      2.m3q1 |  -1111.448    155.307    -7.16   0.000     -1420.04   -802.8565
        trt2 |   733.8042   249.1543     2.95   0.004       238.74    1228.868
        trt3 |   448.2044   279.0905     1.61   0.112    -106.3425    1002.751
        trt4 |   402.0221    215.771     1.86   0.066    -26.71024    830.7544
       _cons |   114.1124   481.3333     0.24   0.813    -842.2866    1070.511
------------------------------------------------------------------------------

. test _b[trt2]=_b[trt4]

 ( 1)  trt2 - trt4 = 0

       F(  1,    89) =    1.88
            Prob > F =    0.1738

. test _b[trt3]=_b[trt4]

 ( 1)  trt3 - trt4 = 0

       F(  1,    89) =    0.03
            Prob > F =    0.8625

. test _b[trt2]=_b[trt3]

 ( 1)  trt2 - trt3 = 0

       F(  1,    89) =    1.06
            Prob > F =    0.3067

. test _b[trt2] + _b[trt3] =_b[trt4]

 ( 1)  trt2 + trt3 - trt4 = 0

       F(  1,    89) =    4.18
            Prob > F =    0.0438

. 
. 
. 
. 
. 
. ** Table C7 ----------------------------------------------------------------
> -----
. *ROBUSTNESS checks - Inference, Multiple Testing, Attrition, LASSO Estimatio
> n
. *POOLED
. ***wild cluster bootstrap, pval
. reg mmtotamt_cust_t1 mmtotamt_cust_t0 i.districtID mage mmarried makan mself
> employed m2q1a i.m3q1 trtment, r level(95)

Linear regression                               Number of obs     =        107
                                                F(15, 91)         =       4.89
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3219
                                                Root MSE          =     827.94

------------------------------------------------------------------------------
             |               Robust
mmtotamt_c~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
mmtotamt_c~0 |   .0090737   .0130858     0.69   0.490    -.0169198    .0350671
             |
  districtID |
          3  |    283.603   345.9106     0.82   0.414    -403.5059    970.7119
          4  |  -119.9951   230.3445    -0.52   0.604    -577.5462    337.5559
          5  |   802.4011   253.5242     3.16   0.002     298.8064    1305.996
          6  |   135.6977   432.8211     0.31   0.755    -724.0483    995.4437
          7  |   1023.136   472.6141     2.16   0.033     84.34595    1961.925
          8  |   877.9332   287.7446     3.05   0.003      306.364    1449.502
          9  |  -28.78967   258.9183    -0.11   0.912    -543.0991    485.5198
             |
        mage |   12.79577    15.1114     0.85   0.399    -17.22117    42.81271
    mmarried |   127.7077   246.3656     0.52   0.605    -361.6674    617.0827
       makan |   18.26443    210.453     0.09   0.931    -399.7746    436.3035
mselfemplo~d |   132.0561   198.7001     0.66   0.508    -262.6371    526.7494
       m2q1a |  -28.82207   47.71181    -0.60   0.547    -123.5957    65.95158
      2.m3q1 |  -196.2415   168.0294    -1.17   0.246    -530.0113    137.5284
     trtment |   436.6255   178.4692     2.45   0.016     82.11837    791.1327
       _cons |   81.32199   466.2873     0.17   0.862    -844.9005    1007.545
------------------------------------------------------------------------------

. boottest trt, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, Rademacher weigh
> ts:
  trt

                           t(91) =     2.4465
                        Prob>|t| =     0.0150

95% confidence set for null hypothesis expression: [74.12, 786.7]

. reg bus_exit i.districtID mage mmarried makan mselfemployed m2q1a i.m3q1 trt
> ment, r level(95)

Linear regression                               Number of obs     =        129
                                                F(15, 113)        =     245.00
                                                Prob > F          =     0.0000
                                                R-squared         =     0.5634
                                                Root MSE          =     .26551

------------------------------------------------------------------------------
             |               Robust
    bus_exit | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |   -.888054   .1277835    -6.95   0.000    -1.141216   -.6348917
          3  |  -.7075964   .1464257    -4.83   0.000    -.9976922   -.4175006
          4  |  -.9744862   .0742349   -13.13   0.000    -1.121559   -.8274135
          5  |  -1.027481   .0449185   -22.87   0.000    -1.116473   -.9384898
          6  |  -1.008338   .0453413   -22.24   0.000    -1.098168    -.918509
          7  |  -.8882956   .1219901    -7.28   0.000     -1.12998   -.6466112
          8  |  -.9993148    .031583   -31.64   0.000    -1.061887   -.9367432
          9  |  -.8785834    .081038   -10.84   0.000    -1.039134   -.7180326
             |
        mage |  -.0041058   .0067582    -0.61   0.545     -.017495    .0092833
    mmarried |   .0317779   .0925833     0.34   0.732    -.1516463    .2152022
       makan |   .0131218   .0524002     0.25   0.803    -.0906925     .116936
mselfemplo~d |   .0279765   .0589801     0.47   0.636    -.0888737    .1448266
       m2q1a |  -.0028854   .0095305    -0.30   0.763    -.0217671    .0159963
      2.m3q1 |   .0343929   .0648426     0.53   0.597    -.0940719    .1628578
     trtment |  -.0690033   .0585168    -1.18   0.241    -.1849357    .0469292
       _cons |   1.143297   .1815037     6.30   0.000     .7837051    1.502888
------------------------------------------------------------------------------

. boottest trt, rep($bootstrap_reps) level(95) nogr seed(1546)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, Rademacher weigh
> ts:
  trt

                          t(113) =    -1.1792
                        Prob>|t| =     0.2450

95% confidence set for null hypothesis expression: [−.1896, .0537]

. **randomization inf: permuntation test, pval
. ritest trtment _b[trtment], reps($bootstrap_reps) strata(districtID) seed(54
> 6): reg mmtotamt_cust_t1 mmtotamt_cust_t0 i.districtID mage mmarried makan m
> selfemployed m2q1a i.m3q1 trtment
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       107
-------------+----------------------------------   F(15, 91)       =      2.88
       Model |  29617226.1        15  1974481.74   Prob > F        =    0.0010
    Residual |  62378651.4        91  685479.686   R-squared       =    0.3219
-------------+----------------------------------   Adj R-squared   =    0.2102
       Total |  91995877.5       106  867885.637   Root MSE        =    827.94

------------------------------------------------------------------------------
mmtotamt_c~1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
mmtotamt_c~0 |   .0090737   .0195169     0.46   0.643    -.0296943    .0478417
             |
  districtID |
          3  |    283.603   457.0995     0.62   0.537     -624.369    1191.575
          4  |  -119.9951   378.0272    -0.32   0.752    -870.8998    630.9095
          5  |   802.4011   454.8673     1.76   0.081    -101.1369    1705.939
          6  |   135.6977   419.5193     0.32   0.747    -697.6259    969.0213
          7  |   1023.136    441.889     2.32   0.023     145.3776    1900.894
          8  |   877.9332   360.3451     2.44   0.017      162.152    1593.714
          9  |  -28.78967   375.9884    -0.08   0.939    -775.6444    718.0651
             |
        mage |   12.79577   16.49178     0.78   0.440    -19.96312    45.55467
    mmarried |   127.7077   262.6495     0.49   0.628    -394.0134    649.4287
       makan |   18.26443   188.3035     0.10   0.923    -355.7774    392.3062
mselfemplo~d |   132.0561   209.7082     0.63   0.530    -284.5035    548.6158
       m2q1a |  -28.82207    47.9893    -0.60   0.550    -124.1469    66.50278
      2.m3q1 |  -196.2415   219.2035    -0.90   0.373    -631.6623    239.1793
     trtment |   436.6255   195.4987     2.23   0.028     48.29142    824.9597
       _cons |   81.32199   513.9433     0.16   0.875    -939.5633    1102.207
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000

      Command: regress mmtotamt_cust_t1 mmtotamt_cust_t0 i.districtID mage
                   mmarried makan mselfemployed m2q1a i.m3q1 trtment
        _pm_1: _b[trtment]
  res. var(s):  trtment
   Resampling:  Permuting trtment
Clust. var(s):  __000000
     Clusters:  130
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |   436.6255      25    1000  0.0250  0.0049  .0162425   .0366848
------------------------------------------------------------------------------
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. ritest trtment _b[trtment], reps($bootstrap_reps) strata(districtID) seed(54
> 6): reg bus_exit i.districtID mage mmarried makan mselfemployed m2q1a i.m3q1
>  trtment
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       129
-------------+----------------------------------   F(15, 113)      =      9.72
       Model |   10.281844        15  .685456268   Prob > F        =    0.0000
    Residual |    7.966218       113  .070497504   R-squared       =    0.5634
-------------+----------------------------------   Adj R-squared   =    0.5055
       Total |   18.248062       128  .142562984   Root MSE        =    .26551

------------------------------------------------------------------------------
    bus_exit | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |   -.888054   .1239854    -7.16   0.000    -1.133691   -.6424166
          3  |  -.7075964   .1177822    -6.01   0.000    -.9409442   -.4742486
          4  |  -.9744862   .1046991    -9.31   0.000    -1.181914   -.7670584
          5  |  -1.027481   .1357906    -7.57   0.000    -1.296507   -.7584559
          6  |  -1.008338     .12386    -8.14   0.000    -1.253727   -.7629492
          7  |  -.8882956   .1301489    -6.83   0.000    -1.146144   -.6304472
          8  |  -.9993148   .0990667   -10.09   0.000    -1.195584   -.8030458
          9  |  -.8785834   .0972633    -9.03   0.000    -1.071279   -.6858873
             |
        mage |  -.0041058   .0039053    -1.05   0.295    -.0118429    .0036312
    mmarried |   .0317779    .068303     0.47   0.643    -.1035427    .1670986
       makan |   .0131218   .0536952     0.24   0.807    -.0932582    .1195017
mselfemplo~d |   .0279765   .0596663     0.47   0.640    -.0902333    .1461862
       m2q1a |  -.0028854    .013955    -0.21   0.837    -.0305328     .024762
      2.m3q1 |   .0343929   .0635663     0.54   0.590    -.0915434    .1603292
     trtment |  -.0690033   .0547324    -1.26   0.210     -.177438    .0394315
       _cons |   1.143297    .146583     7.80   0.000     .8528895    1.433704
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000

      Command: regress bus_exit i.districtID mage mmarried makan
                   mselfemployed m2q1a i.m3q1 trtment
        _pm_1: _b[trtment]
  res. var(s):  trtment
   Resampling:  Permuting trtment
Clust. var(s):  __000000
     Clusters:  130
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |  -.0690033     195    1000  0.1950  0.0125  .1708704   .2209396
------------------------------------------------------------------------------
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. **mht: implement Romano-Wolf (2005) procedure, pval
. rwolf mmtotamt_cust_t1 bus_exit nonmmtotamt_cust_t1 totamt_cust_t1, indepvar
> (trtment trt2 trt3 trt4) reps($bootstrap_reps) seed(124) controls(i.district
> ID cfemale cage cmarried cakan cselfemployed cEducAny cselfIncome) //family 
> (all 3 sales measures: momo; non-momo; total + bus exit)
Bootstrap replications (1000). This may take some time.
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..................................................     50
..................................................     100
..................................................     150
..................................................     200
..................................................     250
..................................................     300
..................................................     350
..................................................     400
..................................................     450
..................................................     500
..................................................     550
..................................................     600
..................................................     650
..................................................     700
..................................................     750
..................................................     800
..................................................     850
..................................................     900
..................................................     950
..................................................     1000




Romano-Wolf step-down adjusted p-values


Independent variable:  trtment
Outcome variables:   mmtotamt_cust_t1 bus_exit nonmmtotamt_cust_t1 totamt_cust
> _t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
   mmtotamt_cust_t1 |     0.2237             0.1908              0.4945
           bus_exit |     0.7458             0.7682              0.8122
nonmmtotamt_cust_t1 |     0.1675             0.2148              0.4945
     totamt_cust_t1 |     0.5621             0.5385              0.8122
------------------------------------------------------------------------------


Independent variable:  trt2
Outcome variables:   mmtotamt_cust_t1 bus_exit nonmmtotamt_cust_t1 totamt_cust
> _t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
   mmtotamt_cust_t1 |     0.5026             0.5065              0.7732
           bus_exit |     0.5075             0.5065              0.7732
nonmmtotamt_cust_t1 |     0.3733             0.4106              0.7732
     totamt_cust_t1 |     0.0873             0.0859              0.3037
------------------------------------------------------------------------------


Independent variable:  trt3
Outcome variables:   mmtotamt_cust_t1 bus_exit nonmmtotamt_cust_t1 totamt_cust
> _t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
   mmtotamt_cust_t1 |     0.8112             0.8422              0.9600
           bus_exit |     0.1768             0.1808              0.5335
nonmmtotamt_cust_t1 |     0.3330             0.3127              0.6663
     totamt_cust_t1 |     0.9115             0.9131              0.9600
------------------------------------------------------------------------------


Independent variable:  trt4
Outcome variables:   mmtotamt_cust_t1 bus_exit nonmmtotamt_cust_t1 totamt_cust
> _t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
   mmtotamt_cust_t1 |          .             0.0010              0.0010
           bus_exit |          .             0.0010              0.0010
nonmmtotamt_cust_t1 |          .             0.0010              0.0010
     totamt_cust_t1 |          .             0.0010              0.0010
------------------------------------------------------------------------------



. **attrition bounds
. **1. [Lee Bounds]**
. leebounds mmtotamt_cust_t1 trtment, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   130
Number of selected obs.            =   107
Trimming porportion                =   0.0663
Effect 95% conf. interval          : [-2.0e+02  844.5984]

------------------------------------------------------------------------------
mmtotamt_c~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trtment      |
       lower |    242.978   262.6178     0.93   0.355    -271.7435    757.6995
       upper |   486.9298   211.7819     2.30   0.021      71.8449    902.0147
------------------------------------------------------------------------------

. // leebounds bus_exit trtment, level(95) cieffect tight() 
. **2. [Behajel et al. Bounds]**
. gen attempts= v0a
(23 missing values generated)

. bys trtment: tab attempts

------------------------------------------------------------------------------
-> trtment = 0

   attempts |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         17       68.00       68.00
          2 |          3       12.00       80.00
          3 |          3       12.00       92.00
          4 |          1        4.00       96.00
          6 |          1        4.00      100.00
------------+-----------------------------------
      Total |         25      100.00

------------------------------------------------------------------------------
-> trtment = 1

   attempts |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         42       51.22       51.22
          2 |         15       18.29       69.51
          3 |          8        9.76       79.27
          4 |         12       14.63       93.90
          5 |          3        3.66       97.56
          7 |          2        2.44      100.00
------------+-----------------------------------
      Total |         82      100.00


. **with 4 or less phone /contact attempts: ctr has 96% response rate, trt has
>  94% response rate
. **use number of attempts - "effort" to rank & bound te
. **so trim (94-96)/94 =2% of trt group, x 82= 2 vendors out
. **Simply trim as follows:
. *(drop bus_exit, makes no sense b/cx 129/130)
. foreach x of varlist mmtotamt_cust_t1  {
  2.         preserve
  3.                 display "`x'"
  4.                 gen itemA= `x' if trtment==1 & attempts<=4 
  5.                 egen iranklo_Aa =rank(itemA) if trtment==1, unique //from
>  above
  6.                 egen iranklo_Ab =rank(-itemA) if trtment==1, unique //fro
> m below
  7.                 gen yupperA= `x'
  8.                 replace yupperA=. if (trtment==1 & iranklo_Aa<=2) | (trtm
> ent==1 & attempts>4) //trim differences within 3 attempts and cut off all ab
> ove 3-attempts
  9.                 gen ylowerA= `x'
 10.                 replace ylowerA=. if (trtment==1 & iranklo_Ab<=2) | (trtm
> ent==1 & attempts>4)
 11.                 reg ylowerA  trtment, r
 12.                 reg yupperA trtment, r
 13.         restore
 14. } 
mmtotamt_cust_t1
(53 missing values generated)
(53 missing values generated)
(53 missing values generated)
(23 missing values generated)
(7 real changes made, 7 to missing)
(23 missing values generated)
(7 real changes made, 7 to missing)

Linear regression                               Number of obs     =        100
                                                F(1, 98)          =       4.77
                                                Prob > F          =     0.0314
                                                R-squared         =     0.0343
                                                Root MSE          =     858.58

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     trtment |   370.0533   169.4651     2.18   0.031     33.75536    706.3513
       _cons |     792.76    132.533     5.98   0.000     529.7526    1055.767
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        100
                                                F(1, 98)          =       7.18
                                                Prob > F          =     0.0086
                                                R-squared         =     0.0477
                                                Root MSE          =     914.19

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     trtment |     467.84   174.5591     2.68   0.009     121.4331    814.2469
       _cons |     792.76    132.533     5.98   0.000     529.7526    1055.767
------------------------------------------------------------------------------

. *
. 
. **SEPARATE
. ***wild cluster bootstrap, pval
. reg mmtotamt_cust_t1 mmtotamt_cust_t0 i.districtID mage mmarried makan mself
> employed m2q1a i.m3q1 trt2 trt3 trt4, r level(95)

Linear regression                               Number of obs     =        107
                                                F(17, 89)         =       4.89
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3258
                                                Root MSE          =     834.78

------------------------------------------------------------------------------
             |               Robust
mmtotamt_c~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
mmtotamt_c~0 |   .0089991   .0136186     0.66   0.510    -.0180609     .036059
             |
  districtID |
          3  |   304.2369   351.7603     0.86   0.389    -394.7032    1003.177
          4  |  -117.8598   244.2393    -0.48   0.631    -603.1582    367.4386
          5  |   808.8636   271.2873     2.98   0.004     269.8215    1347.906
          6  |   147.2044   434.0726     0.34   0.735    -715.2885    1009.697
          7  |   1027.456   483.7044     2.12   0.036     66.34578    1988.567
          8  |   893.3885   302.6474     2.95   0.004     292.0346    1494.742
          9  |  -18.77056    270.872    -0.07   0.945    -556.9875    519.4464
             |
        mage |   13.43771     15.658     0.86   0.393     -17.6744    44.54981
    mmarried |   107.6609   254.8499     0.42   0.674    -398.7204    614.0422
       makan |   4.335822   208.1801     0.02   0.983    -409.3135    417.9852
mselfemplo~d |   128.8225   202.6518     0.64   0.527    -273.8423    531.4873
       m2q1a |   -27.7618   49.09302    -0.57   0.573    -125.3086    69.78499
      2.m3q1 |  -173.8562   174.0911    -1.00   0.321    -519.7716    172.0592
        trt2 |   523.6426   222.0469     2.36   0.021     82.44013     964.845
        trt3 |   418.4564   259.8872     1.61   0.111     -97.9339    934.8466
        trt4 |   358.1305   198.1316     1.81   0.074    -35.55279    751.8137
       _cons |   65.63253    484.323     0.14   0.893    -896.7069    1027.972
------------------------------------------------------------------------------

. boottest trt2, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, Rademacher weigh
> ts:
  trt2

                           t(89) =     2.3583
                        Prob>|t| =     0.0250

95% confidence set for null hypothesis expression: [66.23, 968.9]

. boottest trt3, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, Rademacher weigh
> ts:
  trt3

                           t(89) =     1.6101
                        Prob>|t| =     0.1140

95% confidence set for null hypothesis expression: [−100.1, 968.7]

. boottest trt4, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, Rademacher weigh
> ts:
  trt4

                           t(89) =     1.8075
                        Prob>|t| =     0.0560

95% confidence set for null hypothesis expression: [−4.866, 702.5]

. reg bus_exit i.districtID mage mmarried makan mselfemployed m2q1a i.m3q1 trt
> 2 trt3 trt4, r level(95)

Linear regression                               Number of obs     =        129
                                                F(17, 111)        =     110.07
                                                Prob > F          =     0.0000
                                                R-squared         =     0.5711
                                                Root MSE          =     .26555

------------------------------------------------------------------------------
             |               Robust
    bus_exit | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |   -.886977   .1243956    -7.13   0.000    -1.133475   -.6404788
          3  |  -.7110293   .1452536    -4.90   0.000     -.998859   -.4231995
          4  |  -.9725987   .0754712   -12.89   0.000     -1.12215   -.8230475
          5  |  -1.032394   .0497195   -20.76   0.000    -1.130916   -.9338711
          6  |  -1.011908   .0500678   -20.21   0.000     -1.11112   -.9126951
          7  |   -.893687   .1184032    -7.55   0.000    -1.128311   -.6590631
          8  |  -1.000709   .0363911   -27.50   0.000    -1.072821    -.928598
          9  |  -.8759238   .0846662   -10.35   0.000    -1.043695   -.7081521
             |
        mage |   -.003868   .0068815    -0.56   0.575    -.0175041    .0097681
    mmarried |   .0299913   .0934551     0.32   0.749    -.1551961    .2151787
       makan |   .0126816    .053343     0.24   0.813    -.0930211    .1183843
mselfemplo~d |   .0328368   .0580491     0.57   0.573    -.0821914    .1478651
       m2q1a |  -.0032427   .0093909    -0.35   0.731    -.0218514     .015366
      2.m3q1 |   .0280677   .0671684     0.42   0.677     -.105031    .1611663
        trt2 |  -.1000416   .0608674    -1.64   0.103    -.2206543    .0205712
        trt3 |   -.094358   .0638177    -1.48   0.142    -.2208171     .032101
        trt4 |   -.017767   .0760564    -0.23   0.816    -.1684779    .1329439
       _cons |    1.13774   .1854703     6.13   0.000     .7702182    1.505262
------------------------------------------------------------------------------

. boottest trt2, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, Rademacher weigh
> ts:
  trt2

                          t(111) =    -1.6436
                        Prob>|t| =     0.0980

95% confidence set for null hypothesis expression: [−.2223, .0235]

. boottest trt3, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, Rademacher weigh
> ts:
  trt3

                          t(111) =    -1.4786
                        Prob>|t| =     0.1350

95% confidence set for null hypothesis expression: [−.2229, .03688]

. boottest trt4, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, Rademacher weigh
> ts:
  trt4

                          t(111) =    -0.2336
                        Prob>|t| =     0.8100

95% confidence set for null hypothesis expression: [−.1757, .1429]

. **randomization inf: permuntation test, pval
. ritest  trt2 trt3 trt4 _b[trt2] _b[trt3] _b[trt4], reps($bootstrap_reps) str
> ata(districtID) seed(546): reg mmtotamt_cust_t1 mmtotamt_cust_t0 i.districtI
> D mage mmarried makan mselfemployed m2q1a i.m3q1 trt2 trt3 trt4
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       107
-------------+----------------------------------   F(17, 89)       =      2.53
       Model |  29975610.5        17   1763271.2   Prob > F        =    0.0025
    Residual |    62020267        89  696856.933   R-squared       =    0.3258
-------------+----------------------------------   Adj R-squared   =    0.1971
       Total |  91995877.5       106  867885.637   Root MSE        =    834.78

------------------------------------------------------------------------------
mmtotamt_c~1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
mmtotamt_c~0 |   .0089991   .0196939     0.46   0.649    -.0301322    .0481304
             |
  districtID |
          3  |   304.2369   462.4441     0.66   0.512    -614.6296    1223.104
          4  |  -117.8598   381.3781    -0.31   0.758      -875.65    639.9304
          5  |   808.8636   460.0436     1.76   0.082    -105.2331     1722.96
          6  |   147.2044   424.2787     0.35   0.729    -695.8284    990.2372
          7  |   1027.456   445.9815     2.30   0.024     141.3004    1913.612
          8  |   893.3885   364.1076     2.45   0.016     169.9145    1616.863
          9  |  -18.77056   379.9244    -0.05   0.961    -773.6723    736.1312
             |
        mage |   13.43771   16.76049     0.80   0.425    -19.86502    46.74044
    mmarried |   107.6609   269.2629     0.40   0.690    -427.3587    642.6805
       makan |   4.335822    192.025     0.02   0.982    -377.2138    385.8855
mselfemplo~d |   128.8225   211.5171     0.61   0.544    -291.4575    549.1024
       m2q1a |   -27.7618   48.46726    -0.57   0.568    -124.0652    68.54161
      2.m3q1 |  -173.8562   224.9302    -0.77   0.442    -620.7878    273.0754
        trt2 |   523.6426   236.0552     2.22   0.029     54.60588    992.6793
        trt3 |   418.4564   243.8683     1.72   0.090    -66.10486    903.0176
        trt4 |   358.1305    235.864     1.52   0.132    -110.5262    826.7872
       _cons |   65.63253   518.6962     0.13   0.900    -965.0058    1096.271
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000
Warning: 100% of the resampled realizations for _pm_1 are exactly identical to
>  original value
Warning: 100% of the resampled realizations for _pm_2 are exactly identical to
>  original value

      Command: regress mmtotamt_cust_t1 mmtotamt_cust_t0 i.districtID mage
                   mmarried makan mselfemployed m2q1a i.m3q1 trt2 trt3 trt4
        _pm_1: trt3
        _pm_2: trt4
        _pm_3: _b[trt2]
        _pm_4: _b[trt3]
        _pm_5: _b[trt4]
  res. var(s):  trt2
   Resampling:  Permuting trt2
Clust. var(s):  __000000
     Clusters:  130
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_2 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_3 |   523.6426      10    1000  0.0100  0.0031  .0048055   .0183132
       _pm_4 |   418.4564       0    1000  0.0000  0.0000         0   .0036821
       _pm_5 |   358.1305       0    1000  0.0000  0.0000         0   .0036821
------------------------------------------------------------------------------
Note: Confidence intervals are with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. ritest  trt2 trt3 trt4 _b[trt2] _b[trt3] _b[trt4], reps($bootstrap_reps) str
> ata(districtID) seed(546): reg bus_exit i.districtID mage mmarried makan mse
> lfemployed m2q1a i.m3q1 trt2 trt3 trt4
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       129
-------------+----------------------------------   F(17, 111)      =      8.69
       Model |  10.4207459        17   .61298505   Prob > F        =    0.0000
    Residual |  7.82731617       111  .070516362   R-squared       =    0.5711
-------------+----------------------------------   Adj R-squared   =    0.5054
       Total |   18.248062       128  .142562984   Root MSE        =    .26555

------------------------------------------------------------------------------
    bus_exit | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  districtID |
          2  |   -.886977   .1240303    -7.15   0.000    -1.132751   -.6412026
          3  |  -.7110293   .1179434    -6.03   0.000     -.944742   -.4773166
          4  |  -.9725987   .1047874    -9.28   0.000    -1.180242   -.7649555
          5  |  -1.032394    .136208    -7.58   0.000    -1.302299   -.7624885
          6  |  -1.011908   .1239939    -8.16   0.000     -1.25761   -.7662055
          7  |   -.893687     .13032    -6.86   0.000    -1.151925   -.6354493
          8  |  -1.000709   .0990855   -10.10   0.000    -1.197054   -.8043648
          9  |  -.8759238   .0973406    -9.00   0.000    -1.068811   -.6830369
             |
        mage |   -.003868   .0039763    -0.97   0.333    -.0117474    .0040114
    mmarried |   .0299913   .0698664     0.43   0.669    -.1084537    .1684363
       makan |   .0126816   .0541782     0.23   0.815    -.0946762    .1200394
mselfemplo~d |   .0328368   .0597838     0.55   0.584    -.0856287    .1513024
       m2q1a |  -.0032427   .0139632    -0.23   0.817    -.0309118    .0244263
      2.m3q1 |   .0280677   .0643499     0.44   0.664    -.0994459    .1555812
        trt2 |  -.1000416   .0679742    -1.47   0.144    -.2347369    .0346538
        trt3 |   -.094358   .0683998    -1.38   0.171    -.2298968    .0411807
        trt4 |   -.017767   .0658029    -0.27   0.788    -.1481598    .1126259
       _cons |    1.13774   .1469929     7.74   0.000     .8464636    1.429016
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000
Warning: 100% of the resampled realizations for _pm_1 are exactly identical to
>  original value
Warning: 100% of the resampled realizations for _pm_2 are exactly identical to
>  original value

      Command: regress bus_exit i.districtID mage mmarried makan
                   mselfemployed m2q1a i.m3q1 trt2 trt3 trt4
        _pm_1: trt3
        _pm_2: trt4
        _pm_3: _b[trt2]
        _pm_4: _b[trt3]
        _pm_5: _b[trt4]
  res. var(s):  trt2
   Resampling:  Permuting trt2
Clust. var(s):  __000000
     Clusters:  130
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_2 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_3 |  -.1000416     102    1000  0.1020  0.0096  .0839355   .1224452
       _pm_4 |   -.094358       0    1000  0.0000  0.0000         0   .0036821
       _pm_5 |   -.017767     979    1000  0.9790  0.0045  .9680777   .9869548
------------------------------------------------------------------------------
Note: Confidence intervals are with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. **mht: implement Romano-Wolf (2005) procedure, pval
. rwolf mmtotamt_cust_t1 bus_exit nonmmtotamt_cust_t1 totamt_cust_t1, indepvar
> (trt2 trt3 trt4) reps($bootstrap_reps) seed(124) controls(i.districtID cfema
> le cage cmarried cakan cselfemployed cEducAny cselfIncome) //family (all 3 s
> ales measures: momo; non-momo; total + bus exit)
Bootstrap replications (1000). This may take some time.
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..................................................     50
..................................................     100
..................................................     150
..................................................     200
..................................................     250
..................................................     300
..................................................     350
..................................................     400
..................................................     450
..................................................     500
..................................................     550
..................................................     600
..................................................     650
..................................................     700
..................................................     750
..................................................     800
..................................................     850
..................................................     900
..................................................     950
..................................................     1000




Romano-Wolf step-down adjusted p-values


Independent variable:  trt2
Outcome variables:   mmtotamt_cust_t1 bus_exit nonmmtotamt_cust_t1 totamt_cust
> _t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
   mmtotamt_cust_t1 |     0.0639             0.0450              0.1039
           bus_exit |     0.3363             0.3506              0.3506
nonmmtotamt_cust_t1 |     0.0264             0.0390              0.0809
     totamt_cust_t1 |     0.0263             0.0270              0.0809
------------------------------------------------------------------------------


Independent variable:  trt3
Outcome variables:   mmtotamt_cust_t1 bus_exit nonmmtotamt_cust_t1 totamt_cust
> _t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
   mmtotamt_cust_t1 |     0.1454             0.1578              0.2857
           bus_exit |     0.1077             0.0819              0.2797
nonmmtotamt_cust_t1 |     0.6402             0.6384              0.6813
     totamt_cust_t1 |     0.4885             0.4705              0.6813
------------------------------------------------------------------------------


Independent variable:  trt4
Outcome variables:   mmtotamt_cust_t1 bus_exit nonmmtotamt_cust_t1 totamt_cust
> _t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
   mmtotamt_cust_t1 |     0.2237             0.1928              0.4995
           bus_exit |     0.7458             0.7802              0.8282
nonmmtotamt_cust_t1 |     0.1675             0.2308              0.4995
     totamt_cust_t1 |     0.5621             0.5435              0.8282
------------------------------------------------------------------------------



. **attrition bounds
. **1. [Lee Bounds]**
. foreach x of varlist trt2 trt3 trt4 {
  2.         leebounds mmtotamt_cust_t1 `x', level(95) cieffect tight() 
  3. }

Lee (2009) treatment effect bounds

Number of obs.                     =   130
Number of selected obs.            =   107
Trimming porportion                =   0.0245
Effect 95% conf. interval          : [-3.7e+02  696.5954]

------------------------------------------------------------------------------
mmtotamt_c~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt2         |
       lower |   192.4114   304.5141     0.63   0.527    -404.4252     789.248
       upper |   279.7734   226.9217     1.23   0.218     -164.985    724.5318
------------------------------------------------------------------------------

Lee (2009) treatment effect bounds

Number of obs.                     =   130
Number of selected obs.            =   107
Trimming porportion                =   0.0787
Effect 95% conf. interval          : [-6.9e+02  614.6041]

------------------------------------------------------------------------------
mmtotamt_c~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt3         |
       lower |  -125.1147   335.5255    -0.37   0.709    -782.7327    532.5032
       upper |   177.8426   258.0031     0.69   0.491    -327.8343    683.5194
------------------------------------------------------------------------------

Lee (2009) treatment effect bounds

Number of obs.                     =   130
Number of selected obs.            =   107
Trimming porportion                =   0.0380
Effect 95% conf. interval          : [-4.1e+02  747.5764]

------------------------------------------------------------------------------
mmtotamt_c~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt4         |
       lower |   3.990602   233.0432     0.02   0.986    -452.7657    460.7469
       upper |   148.2538   335.0317     0.44   0.658    -508.3963    804.9038
------------------------------------------------------------------------------

. *
. foreach x of varlist trt2 trt3 trt4 {
  2.         cap leebounds bus_exit `x', level(95) cieffect tight() 
  3. }

. *
. /* dropped to save table space
> **2. [Behajel et al. Bounds]**
> gen attempts= v0a
> bys trtment: tab attempts
> **with 4 or less phone /contact attempts: ctr has 96% response rate, trt has
>  94% response rate
> **use number of attempts - "effort" to rank & bound te
> **so trim (94-96)/94 =2% of trt group, x 82= 2 vendors out
> **Simply trim as follows:
> *(drop bus_exit, makes no sense b/cx 129/130)
> foreach x of varlist mmtotamt_cust_t1  {
> preserve
> display "`x'"
> gen itemA= `x' if trt2==1 & attempts<=4 
> egen iranklo_Aa =rank(itemA) if trt2==1, unique //from above
> egen iranklo_Ab =rank(-itemA) if trt2==1, unique //from below
> gen yupperA= `x'
> replace yupperA=. if (trt2==1 & iranklo_Aa<=2) | (trt2==1 & attempts>4) //tr
> im differences within 3 attempts and cut off all above 3-attempts
> gen ylowerA= `x'
> replace ylowerA=. if (trt2==1 & iranklo_Ab<=2) | (trt2==1 & attempts>4)
> reg ylowerA  trt2, r
> reg yupperA trt2, r
> restore
>                 } 
> *
> foreach x of varlist mmtotamt_cust_t1  {
> preserve
> display "`x'"
> gen itemA= `x' if trt3==1 & attempts<=4 
> egen iranklo_Aa =rank(itemA) if trt3==1, unique //from above
> egen iranklo_Ab =rank(-itemA) if trt3==1, unique //from below
> gen yupperA= `x'
> replace yupperA=. if (trt3==1 & iranklo_Aa<=2) | (trt3==1 & attempts>4) //tr
> im differences within 3 attempts and cut off all above 3-attempts
> gen ylowerA= `x'
> replace ylowerA=. if (trt3==1 & iranklo_Ab<=2) | (trt3==1 & attempts>4)
> reg ylowerA  trt3, r
> reg yupperA trt3, r
> restore
>                 } 
> *
> foreach x of varlist mmtotamt_cust_t1  {
> preserve
> display "`x'"
> gen itemA= `x' if trt4==1 & attempts<=4 
> egen iranklo_Aa =rank(itemA) if trt4==1, unique //from above
> egen iranklo_Ab =rank(-itemA) if trt4==1, unique //from below
> gen yupperA= `x'
> replace yupperA=. if (trt4==1 & iranklo_Aa<=2) | (trt4==1 & attempts>4) //tr
> im differences within 3 attempts and cut off all above 3-attempts
> gen ylowerA= `x'
> replace ylowerA=. if (trt4==1 & iranklo_Ab<=2) | (trt4==1 & attempts>4)
> reg ylowerA trt4, r
> reg yupperA trt4, r
> restore
>                 } 
> */
. *
. 
. 
. 
. ** Table C9 ----------------------------------------------------------------
> -----
. *Robustness checks [SPILLOVER EFFECTS = NON MOMO SALES] - Inference, Multipl
> e Testing, Attrition, LASSO Estimation
. *NON MOMO SALES*
. *bundling w non momo?
. tab m3q1 //75-79% of sample bundled stores

  Currently |
     do you |
offer other |
services at |
       your |
   business |
   center , |
 other than |
      M-Mon |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        102       79.07       79.07
          2 |         27       20.93      100.00
------------+-----------------------------------
      Total |        129      100.00

. tab m3q1 if dropouts==0 

  Currently |
     do you |
offer other |
services at |
       your |
   business |
   center , |
 other than |
      M-Mon |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         85       79.44       79.44
          2 |         22       20.56      100.00
------------+-----------------------------------
      Total |        107      100.00

. *we code non momo sales to 0 for momo only stores, to prevent n changing acr
> oss specs*
. replace nonmmtotamt_cust_t1=0 if m3q1==2 & dropouts==0
(0 real changes made)

. replace nonmmtotamt_cust_t0=0 if m3q1==2 & dropouts==0
(0 real changes made)

. replace totamt_cust_t1=0 if m3q1==2 & dropouts==0
(0 real changes made)

. replace totamt_cust_t0=0 if m3q1==2 & dropouts==0
(0 real changes made)

. 
. sum nonmmtotamt_cust_t1 totamt_cust_t1 if trtment==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
nonmmtotam~1 |         25       135.8    186.8453          0        741
totamt_cus~1 |         25      857.48    772.3202          0       2963

. 
. *POOLED
. ***wild cluster bootstrap, pval
. reg nonmmtotamt_cust_t1 nonmmtotamt_cust_t0 i.districtID mage mmarried makan
>  mselfemployed m2q1a i.m3q1 trtment, r level(95)

Linear regression                               Number of obs     =        107
                                                F(15, 91)         =       3.98
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3964
                                                Root MSE          =      256.7

------------------------------------------------------------------------------
             |               Robust
nonmmtotam~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
nonmmtotam~0 |  -.0001168    .098334    -0.00   0.999    -.1954451    .1952115
             |
  districtID |
          3  |   49.33856    154.179     0.32   0.750    -256.9192    355.5963
          4  |   89.37374   61.26199     1.46   0.148    -32.31568    211.0632
          5  |   93.74165   97.60702     0.96   0.339    -100.1427     287.626
          6  |   559.6679   178.5739     3.13   0.002     204.9529    914.3829
          7  |   246.5865   101.9699     2.42   0.018     44.03582    449.1372
          8  |   111.7278   54.64484     2.04   0.044     3.182535    220.2731
          9  |   7.582423   50.34301     0.15   0.881    -92.41779    107.5826
             |
        mage |  -.6701764   3.652452    -0.18   0.855    -7.925324    6.584971
    mmarried |    26.4209   56.73534     0.47   0.643    -86.27689    139.1187
       makan |   64.14806     56.694     1.13   0.261     -48.4676    176.7637
mselfemplo~d |  -45.46799   53.95695    -0.84   0.402    -152.6469    61.71087
       m2q1a |   23.88877   20.73512     1.15   0.252      -17.299    65.07654
      2.m3q1 |  -214.1722   46.83067    -4.57   0.000    -307.1955   -121.1488
     trtment |   132.7775   58.69281     2.26   0.026      16.1915    249.3636
       _cons |  -24.62476    113.373    -0.22   0.829    -249.8263    200.5768
------------------------------------------------------------------------------

. boottest trt, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, Rademacher weigh
> ts:
  trt

                           t(91) =     2.2622
                        Prob>|t| =     0.0240

95% confidence set for null hypothesis expression: [19.18, 248.1]

. reg totamt_cust_t1 totamt_cust_t0 i.districtID mage mmarried makan mselfempl
> oyed m2q1a i.m3q1 trtment, r level(95)

Linear regression                               Number of obs     =        107
                                                F(15, 91)         =      12.04
                                                Prob > F          =     0.0000
                                                R-squared         =     0.4504
                                                Root MSE          =     908.07

------------------------------------------------------------------------------
             |               Robust
totamt_cus~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
totamt_cus~0 |   .0181485   .0140112     1.30   0.198    -.0096829      .04598
             |
  districtID |
          3  |   247.6569    412.581     0.60   0.550    -571.8845    1067.198
          4  |  -66.11714   204.0018    -0.32   0.747    -471.3416    339.1073
          5  |   633.8501   298.1353     2.13   0.036     41.64098    1226.059
          6  |   644.2233   511.7403     1.26   0.211    -372.2859    1660.733
          7  |   1175.496   507.6937     2.32   0.023     167.0246    2183.967
          8  |   824.1177   267.0767     3.09   0.003     293.6026    1354.633
          9  |  -99.91784    223.885    -0.45   0.656    -544.6379    344.8022
             |
        mage |   13.45511   14.82702     0.91   0.367    -15.99694    42.90717
    mmarried |   57.80315   246.2005     0.23   0.815    -431.2438    546.8501
       makan |   115.9029   233.3411     0.50   0.621    -347.6006    579.4064
mselfemplo~d |   41.56203   197.9912     0.21   0.834    -351.7232    434.8472
       m2q1a |  -10.09182   52.21125    -0.19   0.847    -113.8031    93.61942
      2.m3q1 |  -1168.033   149.2524    -7.83   0.000    -1464.504   -871.5611
     trtment |   537.6268   195.8947     2.74   0.007     148.5061    926.7475
       _cons |   150.5395   461.0437     0.33   0.745    -765.2672    1066.346
------------------------------------------------------------------------------

. boottest trt, rep($bootstrap_reps) level(95) nogr seed(1546)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, Rademacher weigh
> ts:
  trt

                           t(91) =     2.7445
                        Prob>|t| =     0.0030

95% confidence set for null hypothesis expression: [149.1, 926.1]

. **randomization inf: permuntation test, pval
. ritest trtment _b[trtment], reps($bootstrap_reps) strata(districtID) seed(54
> 6): reg nonmmtotamt_cust_t1 nonmmtotamt_cust_t0 i.districtID mage mmarried m
> akan mselfemployed m2q1a i.m3q1 trtment
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       107
-------------+----------------------------------   F(15, 91)       =      3.98
       Model |  3938818.31        15  262587.888   Prob > F        =    0.0000
    Residual |  5996528.88        91  65895.9218   R-squared       =    0.3964
-------------+----------------------------------   Adj R-squared   =    0.2970
       Total |   9935347.2       106  93729.6905   Root MSE        =     256.7

------------------------------------------------------------------------------
nonmmtotam~1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
nonmmtotam~0 |  -.0001168   .1637714    -0.00   0.999    -.3254285     .325195
             |
  districtID |
          3  |   49.33856   144.1495     0.34   0.733    -236.9967    335.6738
          4  |   89.37374   116.5352     0.77   0.445     -142.109    320.8565
          5  |   93.74165   142.3434     0.66   0.512    -189.0059    376.4892
          6  |   559.6679   130.8366     4.28   0.000     299.7771    819.5587
          7  |   246.5865   136.8312     1.80   0.075    -25.21185    518.3849
          8  |   111.7278   111.5106     1.00   0.319    -109.7744      333.23
          9  |   7.582423   116.2986     0.07   0.948    -223.4305    238.5954
             |
        mage |  -.6701764    5.22087    -0.13   0.898    -11.04079     9.70044
    mmarried |    26.4209   81.94026     0.32   0.748    -136.3434    189.1851
       makan |   64.14806   57.79942     1.11   0.270     -50.6634    178.9595
mselfemplo~d |  -45.46799   65.83425    -0.69   0.492    -176.2396    85.30366
       m2q1a |   23.88877   14.80326     1.61   0.110    -5.516085    53.29362
      2.m3q1 |  -214.1722   73.42377    -2.92   0.004    -360.0194   -68.32485
     trtment |   132.7775   60.74507     2.19   0.031     12.11493    253.4402
       _cons |  -24.62476   158.0168    -0.16   0.877    -338.5057    289.2562
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000

      Command: regress nonmmtotamt_cust_t1 nonmmtotamt_cust_t0 i.districtID
                   mage mmarried makan mselfemployed m2q1a i.m3q1 trtment
        _pm_1: _b[trtment]
  res. var(s):  trtment
   Resampling:  Permuting trtment
Clust. var(s):  __000000
     Clusters:  130
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |   132.7775      29    1000  0.0290  0.0053  .0195059   .0413847
------------------------------------------------------------------------------
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. ritest trtment _b[trtment], reps($bootstrap_reps) strata(districtID) seed(54
> 6): reg totamt_cust_t1 totamt_cust_t0 i.districtID mage mmarried makan mself
> employed m2q1a i.m3q1 trtment
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       107
-------------+----------------------------------   F(15, 91)       =      4.97
       Model |  61505979.6        15  4100398.64   Prob > F        =    0.0000
    Residual |  75037706.9        91  824590.186   R-squared       =    0.4504
-------------+----------------------------------   Adj R-squared   =    0.3599
       Total |   136543687       106  1288147.99   Root MSE        =    908.07

------------------------------------------------------------------------------
totamt_cus~1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
totamt_cus~0 |   .0181485   .0218292     0.83   0.408    -.0252124    .0615095
             |
  districtID |
          3  |   247.6569   502.1797     0.49   0.623    -749.8613    1245.175
          4  |  -66.11714   415.4147    -0.16   0.874    -891.2873    759.0531
          5  |   633.8501   498.9902     1.27   0.207    -357.3326    1625.033
          6  |   644.2233    460.116     1.40   0.165    -269.7406    1558.187
          7  |   1175.496   484.6287     2.43   0.017     212.8404    2138.151
          8  |   824.1177   394.5803     2.09   0.040     40.33246    1607.903
          9  |  -99.91784   412.0027    -0.24   0.809    -918.3106    718.4749
             |
        mage |   13.45511   18.03985     0.75   0.458    -22.37883    49.28906
    mmarried |   57.80315    288.104     0.20   0.841      -514.48    630.0863
       makan |   115.9029   206.5412     0.56   0.576    -294.3658    526.1715
mselfemplo~d |   41.56203   230.2182     0.18   0.857    -415.7382    498.8622
       m2q1a |  -10.09182   52.35674    -0.19   0.848    -114.0921    93.90841
      2.m3q1 |  -1168.033   245.4835    -4.76   0.000    -1655.655   -680.4098
     trtment |   537.6268   214.4337     2.51   0.014     111.6807    963.5729
       _cons |   150.5395   563.4583     0.27   0.790    -968.7012     1269.78
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000

      Command: regress totamt_cust_t1 totamt_cust_t0 i.districtID mage
                   mmarried makan mselfemployed m2q1a i.m3q1 trtment
        _pm_1: _b[trtment]
  res. var(s):  trtment
   Resampling:  Permuting trtment
Clust. var(s):  __000000
     Clusters:  130
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |   537.6268      14    1000  0.0140  0.0037  .0076745   .0233782
------------------------------------------------------------------------------
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. **mht: implement Romano-Wolf (2005) procedure, pval
. rwolf nonmmtotamt_cust_t1 totamt_cust_t1, indepvar(trtment trt2 trt3 trt4) r
> eps($bootstrap_reps) seed(124) controls(i.districtID cfemale cage cmarried c
> akan cselfemployed cEducAny cselfIncome) //family (all 3 sales measures: mom
> o; non-momo; total + bus exit)
Bootstrap replications (1000). This may take some time.
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..................................................     50
..................................................     100
..................................................     150
..................................................     200
..................................................     250
..................................................     300
..................................................     350
..................................................     400
..................................................     450
..................................................     500
..................................................     550
..................................................     600
..................................................     650
..................................................     700
..................................................     750
..................................................     800
..................................................     850
..................................................     900
..................................................     950
..................................................     1000




Romano-Wolf step-down adjusted p-values


Independent variable:  trtment
Outcome variables:   nonmmtotamt_cust_t1 totamt_cust_t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
nonmmtotamt_cust_t1 |     0.1675             0.2308              0.3177
     totamt_cust_t1 |     0.5621             0.5435              0.5435
------------------------------------------------------------------------------


Independent variable:  trt2
Outcome variables:   nonmmtotamt_cust_t1 totamt_cust_t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
nonmmtotamt_cust_t1 |     0.3733             0.4136              0.4136
     totamt_cust_t1 |     0.0873             0.0699              0.1618
------------------------------------------------------------------------------


Independent variable:  trt3
Outcome variables:   nonmmtotamt_cust_t1 totamt_cust_t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
nonmmtotamt_cust_t1 |     0.3330             0.3187              0.5365
     totamt_cust_t1 |     0.9115             0.9291              0.9291
------------------------------------------------------------------------------


Independent variable:  trt4
Outcome variables:   nonmmtotamt_cust_t1 totamt_cust_t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
nonmmtotamt_cust_t1 |          .             0.0010              0.0010
     totamt_cust_t1 |          .             0.0010              0.0010
------------------------------------------------------------------------------



. **attrition bounds
. **1. [Lee Bounds]**
. leebounds nonmmtotamt_cust_t1 trtment, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   130
Number of selected obs.            =   107
Trimming porportion                =   0.0663
Effect 95% conf. interval          : [-1.8e+02  204.5626]

------------------------------------------------------------------------------
nonmmtotam~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trtment      |
       lower |   12.61388   112.1231     0.11   0.910    -207.1434    232.3712
       upper |   103.1159   59.46974     1.73   0.083    -13.44262    219.6745
------------------------------------------------------------------------------

. leebounds totamt_cust_t1 trtment, level(95) cieffect tight() 

Lee (2009) treatment effect bounds

Number of obs.                     =   130
Number of selected obs.            =   107
Trimming porportion                =   0.0663
Effect 95% conf. interval          : [-3.2e+02  885.7161]

------------------------------------------------------------------------------
totamt_cus~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trtment      |
       lower |   172.7494   293.4395     0.59   0.556    -402.3814    747.8802
       upper |   463.3624   250.9747     1.85   0.065    -28.53888    955.2638
------------------------------------------------------------------------------

. **2. [Behajel et al. Bounds]**
. // gen attempts= v0a
. bys trtment: tab attempts

------------------------------------------------------------------------------
-> trtment = 0

   attempts |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         17       68.00       68.00
          2 |          3       12.00       80.00
          3 |          3       12.00       92.00
          4 |          1        4.00       96.00
          6 |          1        4.00      100.00
------------+-----------------------------------
      Total |         25      100.00

------------------------------------------------------------------------------
-> trtment = 1

   attempts |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         42       51.22       51.22
          2 |         15       18.29       69.51
          3 |          8        9.76       79.27
          4 |         12       14.63       93.90
          5 |          3        3.66       97.56
          7 |          2        2.44      100.00
------------+-----------------------------------
      Total |         82      100.00


. **with 4 or less phone /contact attempts: ctr has 96% response rate, trt has
>  94% response rate
. **use number of attempts - "effort" to rank & bound te
. **so trim (94-96)/94 =2% of trt group, x 82= 2 vendors out
. **Simply trim as follows:
. *(drop bus_exit, makes no sense b/cx 129/130)
. foreach x of varlist nonmmtotamt_cust_t1 totamt_cust_t1  {
  2.         preserve
  3.                 display "`x'"
  4.                 gen itemA= `x' if trtment==1 & attempts<=4 
  5.                 egen iranklo_Aa =rank(itemA) if trtment==1, unique //from
>  above
  6.                 egen iranklo_Ab =rank(-itemA) if trtment==1, unique //fro
> m below
  7.                 gen yupperA= `x'
  8.                 replace yupperA=. if (trtment==1 & iranklo_Aa<=2) | (trtm
> ent==1 & attempts>4) //trim differences within 3 attempts and cut off all ab
> ove 3-attempts
  9.                 gen ylowerA= `x'
 10.                 replace ylowerA=. if (trtment==1 & iranklo_Ab<=2) | (trtm
> ent==1 & attempts>4)
 11.                 reg ylowerA  trtment, r
 12.                 reg yupperA trtment, r
 13.         restore
 14. } 
nonmmtotamt_cust_t1
(53 missing values generated)
(53 missing values generated)
(53 missing values generated)
(23 missing values generated)
(7 real changes made, 7 to missing)
(23 missing values generated)
(7 real changes made, 7 to missing)

Linear regression                               Number of obs     =        100
                                                F(1, 98)          =       1.80
                                                Prob > F          =     0.1829
                                                R-squared         =     0.0117
                                                Root MSE          =     270.35

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     trtment |   67.26667   50.15247     1.34   0.183    -32.25927    166.7926
       _cons |      135.8   36.98578     3.67   0.000     62.40293    209.1971
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        100
                                                F(1, 98)          =       3.59
                                                Prob > F          =     0.0612
                                                R-squared         =     0.0204
                                                Root MSE          =     311.98

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     trtment |   102.8133   54.28017     1.89   0.061    -4.903904    210.5306
       _cons |      135.8   36.98578     3.67   0.000     62.40293    209.1971
------------------------------------------------------------------------------
totamt_cust_t1
(53 missing values generated)
(53 missing values generated)
(53 missing values generated)
(23 missing values generated)
(7 real changes made, 7 to missing)
(23 missing values generated)
(7 real changes made, 7 to missing)

Linear regression                               Number of obs     =        100
                                                F(1, 98)          =       2.52
                                                Prob > F          =     0.1155
                                                R-squared         =     0.0176
                                                Root MSE          =     1035.6

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     trtment |        317   199.5997     1.59   0.115    -79.09906    713.0991
       _cons |     857.48   152.8797     5.61   0.000     554.0951    1160.865
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        100
                                                F(1, 98)          =       4.46
                                                Prob > F          =     0.0373
                                                R-squared         =     0.0282
                                                Root MSE          =     1131.2

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     trtment |   440.4667   208.6252     2.11   0.037     26.45681    854.4765
       _cons |     857.48   152.8797     5.61   0.000     554.0951    1160.865
------------------------------------------------------------------------------

. *
. 
. **SEPARATE
. ***wild cluster bootstrap, pval
. reg nonmmtotamt_cust_t1 nonmmtotamt_cust_t0 i.districtID mage mmarried makan
>  mselfemployed m2q1a i.m3q1 trt2 trt3 trt4, r level(95)

Linear regression                               Number of obs     =        107
                                                F(17, 89)         =       3.41
                                                Prob > F          =     0.0001
                                                R-squared         =     0.4064
                                                Root MSE          =     257.43

------------------------------------------------------------------------------
             |               Robust
nonmmtotam~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
nonmmtotam~0 |   .0169747     .10317     0.16   0.870    -.1880219    .2219714
             |
  districtID |
          3  |   57.87904   155.8047     0.37   0.711    -251.7017    367.4597
          4  |   84.67787   69.07594     1.23   0.223    -52.57455    221.9303
          5  |   84.74084   98.11872     0.86   0.390     -110.219    279.7007
          6  |   553.3832   177.6757     3.11   0.002     200.3454     906.421
          7  |   240.9122   105.9287     2.27   0.025     30.43403    451.3903
          8  |    111.522   60.89967     1.83   0.070    -9.484317    232.5284
          9  |   1.963584   56.22637     0.03   0.972     -109.757    113.6842
             |
        mage |   .0234265   3.754596     0.01   0.995    -7.436875    7.483728
    mmarried |   9.354933     53.661     0.17   0.862    -97.26832    115.9782
       makan |   54.31131    56.3624     0.96   0.338    -57.67957    166.3022
mselfemplo~d |  -48.35187   53.60412    -0.90   0.369    -154.8621    58.15838
       m2q1a |   23.12076   20.31616     1.14   0.258    -17.24701    63.48853
      2.m3q1 |  -196.9389   52.03943    -3.78   0.000    -300.3401   -93.53761
        trt2 |    167.175   73.40903     2.28   0.025     21.31285    313.0372
        trt3 |   80.51831   65.86129     1.22   0.225    -50.34668    211.3833
        trt4 |   141.4084   76.43945     1.85   0.068    -10.47517     293.292
       _cons |  -31.11423   118.1101    -0.26   0.793    -265.7965     203.568
------------------------------------------------------------------------------

. boottest trt2, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, Rademacher weigh
> ts:
  trt2

                           t(89) =     2.2773
                        Prob>|t| =     0.0290

95% confidence set for null hypothesis expression: [19.08, 310.2]

. boottest trt3, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, Rademacher weigh
> ts:
  trt3

                           t(89) =     1.2225
                        Prob>|t| =     0.2070

95% confidence set for null hypothesis expression: [−42.99, 207.6]

. boottest trt4, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, Rademacher weigh
> ts:
  trt4

                           t(89) =     1.8499
                        Prob>|t| =     0.0720

95% confidence set for null hypothesis expression: [−11.87, 298.1]

. reg totamt_cust_t1 totamt_cust_t0 i.districtID mage mmarried makan mselfempl
> oyed m2q1a i.m3q1 trt2 trt3 trt4, r level(95)

Linear regression                               Number of obs     =        107
                                                F(17, 89)         =      11.40
                                                Prob > F          =     0.0000
                                                R-squared         =     0.4622
                                                Root MSE          =     908.31

------------------------------------------------------------------------------
             |               Robust
totamt_cus~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
totamt_cus~0 |   .0182059   .0145446     1.25   0.214    -.0106938    .0471057
             |
  districtID |
          3  |   297.6754   412.9166     0.72   0.473    -522.7811    1118.132
          4  |  -67.50975   232.3543    -0.29   0.772    -529.1928    394.1733
          5  |   633.7176   322.5525     1.96   0.053    -7.187371    1274.623
          6  |   657.1109   502.2562     1.31   0.194    -340.8614    1655.083
          7  |   1176.759   518.9353     2.27   0.026     145.6452    2207.872
          8  |   851.3329   288.1095     2.95   0.004     278.8654      1423.8
          9  |  -87.66202   238.8683    -0.37   0.714    -562.2882    386.9642
             |
        mage |   15.51271   15.07943     1.03   0.306     -14.4498    45.47523
    mmarried |   1.701359   256.1286     0.01   0.995    -507.2208    510.6235
       makan |   80.10741   228.5388     0.35   0.727    -373.9943    534.2091
mselfemplo~d |   33.74764   201.2889     0.17   0.867    -366.2091    433.7044
       m2q1a |  -8.921722   54.57742    -0.16   0.871    -117.3659    99.52245
      2.m3q1 |  -1111.448    155.307    -7.16   0.000     -1420.04   -802.8565
        trt2 |   733.8042   249.1543     2.95   0.004       238.74    1228.868
        trt3 |   448.2044   279.0905     1.61   0.112    -106.3425    1002.751
        trt4 |   402.0221    215.771     1.86   0.066    -26.71024    830.7544
       _cons |   114.1124   481.3333     0.24   0.813    -842.2866    1070.511
------------------------------------------------------------------------------

. boottest trt2, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, Rademacher weigh
> ts:
  trt2

                           t(89) =     2.9452
                        Prob>|t| =     0.0040

95% confidence set for null hypothesis expression: [225.6, 1246]

. boottest trt3, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, Rademacher weigh
> ts:
  trt3

                           t(89) =     1.6059
                        Prob>|t| =     0.1090

95% confidence set for null hypothesis expression: [−104.4, 1008]

. boottest trt4, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, Rademacher weigh
> ts:
  trt4

                           t(89) =     1.8632
                        Prob>|t| =     0.0460

95% confidence set for null hypothesis expression: [6.426, 777]

. **randomization inf: permuntation test, pval
. ritest  trt2 trt3 trt4 _b[trt2] _b[trt3] _b[trt4], reps($bootstrap_reps) str
> ata(districtID) seed(546): reg nonmmtotamt_cust_t1 nonmmtotamt_cust_t0 i.dis
> trictID mage mmarried makan mselfemployed m2q1a i.m3q1 trt2 trt3 trt4
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       107
-------------+----------------------------------   F(17, 89)       =      3.58
       Model |  4037305.88        17  237488.581   Prob > F        =    0.0000
    Residual |  5898041.31        89  66270.1271   R-squared       =    0.4064
-------------+----------------------------------   Adj R-squared   =    0.2930
       Total |   9935347.2       106  93729.6905   Root MSE        =    257.43

------------------------------------------------------------------------------
nonmmtotam~1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
nonmmtotam~0 |   .0169747   .1653548     0.10   0.918    -.3115818    .3455312
             |
  districtID |
          3  |   57.87904   144.7565     0.40   0.690     -229.749    345.5071
          4  |   84.67787   116.9541     0.72   0.471    -147.7075    317.0632
          5  |   84.74084   143.1831     0.59   0.555     -199.761    369.2427
          6  |   553.3832   131.6271     4.20   0.000      291.843    814.9234
          7  |   240.9122   137.3659     1.75   0.083    -32.03102    513.8554
          8  |    111.522   112.1118     0.99   0.323    -111.2418    334.2858
          9  |   1.963584   116.9002     0.02   0.987    -230.3147    234.2419
             |
        mage |   .0234265   5.266892     0.00   0.996    -10.44178    10.48863
    mmarried |   9.354933   83.39496     0.11   0.911    -156.3491    175.0589
       makan |   54.31131   58.60704     0.93   0.357    -62.13963    170.7622
mselfemplo~d |  -48.35187   66.08585    -0.73   0.466    -179.6631    82.95932
       m2q1a |   23.12076   14.87336     1.55   0.124    -6.432288    52.67381
      2.m3q1 |  -196.9389   75.38785    -2.61   0.011    -346.7329    -47.1448
        trt2 |    167.175   73.36229     2.28   0.025     21.40571    312.9443
        trt3 |   80.51831   75.12491     1.07   0.287    -68.75329    229.7899
        trt4 |   141.4084   72.74896     1.94   0.055    -3.142247     285.959
       _cons |  -31.11423   158.6407    -0.20   0.845      -346.33    284.1015
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000
Warning: 100% of the resampled realizations for _pm_1 are exactly identical to
>  original value
Warning: 100% of the resampled realizations for _pm_2 are exactly identical to
>  original value

      Command: regress nonmmtotamt_cust_t1 nonmmtotamt_cust_t0 i.districtID
                   mage mmarried makan mselfemployed m2q1a i.m3q1 trt2 trt3
                   trt4
        _pm_1: trt3
        _pm_2: trt4
        _pm_3: _b[trt2]
        _pm_4: _b[trt3]
        _pm_5: _b[trt4]
  res. var(s):  trt2
   Resampling:  Permuting trt2
Clust. var(s):  __000000
     Clusters:  130
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_2 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_3 |    167.175       6    1000  0.0060  0.0024   .002205   .0130134
       _pm_4 |   80.51831       0    1000  0.0000  0.0000         0   .0036821
       _pm_5 |   141.4084       0    1000  0.0000  0.0000         0   .0036821
------------------------------------------------------------------------------
Note: Confidence intervals are with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. ritest  trt2 trt3 trt4 _b[trt2] _b[trt3] _b[trt4], reps($bootstrap_reps) str
> ata(districtID) seed(546): reg totamt_cust_t1 totamt_cust_t0 i.districtID ma
> ge mmarried makan mselfemployed m2q1a i.m3q1 trt2 trt3 trt4
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       107
-------------+----------------------------------   F(17, 89)       =      4.50
       Model |  63116205.1        17  3712717.95   Prob > F        =    0.0000
    Residual |  73427481.4        89  825027.881   R-squared       =    0.4622
-------------+----------------------------------   Adj R-squared   =    0.3595
       Total |   136543687       106  1288147.99   Root MSE        =    908.31

------------------------------------------------------------------------------
totamt_cus~1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
totamt_cus~0 |   .0182059   .0218994     0.83   0.408    -.0253076    .0617195
             |
  districtID |
          3  |   297.6754   504.0599     0.59   0.556    -703.8808    1299.232
          4  |  -67.50975   415.7003    -0.16   0.871    -893.4975     758.478
          5  |   633.7176   500.5916     1.27   0.209    -360.9473    1628.383
          6  |   657.1109   461.6782     1.42   0.158    -260.2338    1574.456
          7  |   1176.759   485.1918     2.43   0.017     212.6928    2140.824
          8  |   851.3329   395.4881     2.15   0.034     65.50642    1637.159
          9  |  -87.66202   412.9678    -0.21   0.832    -908.2202    732.8961
             |
        mage |   15.51271    18.1906     0.85   0.396    -20.63162    51.65704
    mmarried |   1.701359    292.992     0.01   0.995    -580.4675    583.8702
       makan |   80.10741   208.9928     0.38   0.702    -335.1568    495.3716
mselfemplo~d |   33.74764    230.368     0.15   0.884    -423.9887     491.484
       m2q1a |  -8.921722   52.46041    -0.17   0.865    -113.1594      95.316
      2.m3q1 |  -1111.448   249.4821    -4.46   0.000    -1607.164   -615.7325
        trt2 |   733.8042   256.8927     2.86   0.005      223.364    1244.245
        trt3 |   448.2044   265.6492     1.69   0.095    -79.63486    976.0436
        trt4 |   402.0221   256.5941     1.57   0.121    -107.8249    911.8691
       _cons |   114.1124   564.2125     0.20   0.840    -1006.966    1235.191
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000
Warning: 100% of the resampled realizations for _pm_1 are exactly identical to
>  original value
Warning: 100% of the resampled realizations for _pm_2 are exactly identical to
>  original value

      Command: regress totamt_cust_t1 totamt_cust_t0 i.districtID mage
                   mmarried makan mselfemployed m2q1a i.m3q1 trt2 trt3 trt4
        _pm_1: trt3
        _pm_2: trt4
        _pm_3: _b[trt2]
        _pm_4: _b[trt3]
        _pm_5: _b[trt4]
  res. var(s):  trt2
   Resampling:  Permuting trt2
Clust. var(s):  __000000
     Clusters:  130
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_2 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_3 |   733.8043       0    1000  0.0000  0.0000         0   .0036821
       _pm_4 |   448.2044       0    1000  0.0000  0.0000         0   .0036821
       _pm_5 |   402.0221       0    1000  0.0000  0.0000         0   .0036821
------------------------------------------------------------------------------
Note: Confidence intervals are with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. **mht: implement Romano-Wolf (2005) procedure, pval
. rwolf mmtotamt_cust_t1 bus_exit nonmmtotamt_cust_t1 totamt_cust_t1, indepvar
> (trt2 trt3 trt4) reps($bootstrap_reps) seed(124) controls(i.districtID cfema
> le cage cmarried cakan cselfemployed cEducAny cselfIncome) //family (all 3 s
> ales measures: momo; non-momo; total + bus exit)
Bootstrap replications (1000). This may take some time.
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..................................................     50
..................................................     100
..................................................     150
..................................................     200
..................................................     250
..................................................     300
..................................................     350
..................................................     400
..................................................     450
..................................................     500
..................................................     550
..................................................     600
..................................................     650
..................................................     700
..................................................     750
..................................................     800
..................................................     850
..................................................     900
..................................................     950
..................................................     1000




Romano-Wolf step-down adjusted p-values


Independent variable:  trt2
Outcome variables:   mmtotamt_cust_t1 bus_exit nonmmtotamt_cust_t1 totamt_cust
> _t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
   mmtotamt_cust_t1 |     0.0639             0.0450              0.1039
           bus_exit |     0.3363             0.3506              0.3506
nonmmtotamt_cust_t1 |     0.0264             0.0390              0.0809
     totamt_cust_t1 |     0.0263             0.0270              0.0809
------------------------------------------------------------------------------


Independent variable:  trt3
Outcome variables:   mmtotamt_cust_t1 bus_exit nonmmtotamt_cust_t1 totamt_cust
> _t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
   mmtotamt_cust_t1 |     0.1454             0.1578              0.2857
           bus_exit |     0.1077             0.0819              0.2797
nonmmtotamt_cust_t1 |     0.6402             0.6384              0.6813
     totamt_cust_t1 |     0.4885             0.4705              0.6813
------------------------------------------------------------------------------


Independent variable:  trt4
Outcome variables:   mmtotamt_cust_t1 bus_exit nonmmtotamt_cust_t1 totamt_cust
> _t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
   mmtotamt_cust_t1 |     0.2237             0.1928              0.4995
           bus_exit |     0.7458             0.7802              0.8282
nonmmtotamt_cust_t1 |     0.1675             0.2308              0.4995
     totamt_cust_t1 |     0.5621             0.5435              0.8282
------------------------------------------------------------------------------



. **attrition bounds
. **1. [Lee Bounds]**
. foreach x of varlist trt2 trt3 trt4 {
  2.         leebounds nonmmtotamt_cust_t1 `x', level(95) cieffect tight() 
  3. }

Lee (2009) treatment effect bounds

Number of obs.                     =   130
Number of selected obs.            =   107
Trimming porportion                =   0.0245
Effect 95% conf. interval          : [-1.7e+02  258.0366]

------------------------------------------------------------------------------
nonmmtotam~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt2         |
       lower |   66.39745   129.0279     0.51   0.607    -186.4925    319.2874
       upper |    99.8419   85.65635     1.17   0.244    -68.04146    267.7252
------------------------------------------------------------------------------

Lee (2009) treatment effect bounds

Number of obs.                     =   130
Number of selected obs.            =   107
Trimming porportion                =   0.0787
Effect 95% conf. interval          : [-2.1e+02  40.8697]

------------------------------------------------------------------------------
nonmmtotam~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt3         |
       lower |  -106.6036   61.05633    -1.75   0.081    -226.2718    13.06457
       upper |  -53.65269   55.70453    -0.96   0.335    -162.8316    55.52619
------------------------------------------------------------------------------

Lee (2009) treatment effect bounds

Number of obs.                     =   130
Number of selected obs.            =   107
Trimming porportion                =   0.0380
Effect 95% conf. interval          : [-94.3922  294.2280]

------------------------------------------------------------------------------
nonmmtotam~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt4         |
       lower |   51.71617   82.04398     0.63   0.528    -109.0871    212.5194
       upper |   101.4135   108.2708     0.94   0.349    -110.7933    313.6204
------------------------------------------------------------------------------

. *
. foreach x of varlist trt2 trt3 trt4 {
  2.         leebounds totamt_cust_t1 `x', level(95) cieffect tight() 
  3. }

Lee (2009) treatment effect bounds

Number of obs.                     =   130
Number of selected obs.            =   107
Trimming porportion                =   0.0245
Effect 95% conf. interval          : [-3.9e+02  1.1e+03]

------------------------------------------------------------------------------
totamt_cus~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt2         |
       lower |   396.7403   429.4172     0.92   0.356    -444.9019    1238.383
       upper |   523.1167   307.3311     1.70   0.089    -79.24127    1125.475
------------------------------------------------------------------------------

Lee (2009) treatment effect bounds

Number of obs.                     =   130
Number of selected obs.            =   107
Trimming porportion                =   0.0787
Effect 95% conf. interval          : [-1.0e+03  530.7645]

------------------------------------------------------------------------------
totamt_cus~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt3         |
       lower |   -341.129   389.0812    -0.88   0.381    -1103.714    421.4562
       upper |   12.15665   306.5323     0.04   0.968    -588.6357     612.949
------------------------------------------------------------------------------

Lee (2009) treatment effect bounds

Number of obs.                     =   130
Number of selected obs.            =   107
Trimming porportion                =   0.0380
Effect 95% conf. interval          : [-5.6e+02  770.0041]

------------------------------------------------------------------------------
totamt_cus~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt4         |
       lower |  -78.58647   270.9962    -0.29   0.772    -609.7293    452.5564
       upper |    96.8609   378.2015     0.26   0.798    -644.4003    838.1221
------------------------------------------------------------------------------

. *
. **2. [Behajel et al. Bounds]**
. *gen attempts= v0a
. *bys trtment: tab attempts
. **with 4 or less phone /contact attempts: ctr has 96% response rate, trt has
>  94% response rate
. **use number of attempts - "effort" to rank & bound te
. **so trim (94-96)/94 =2% of trt group, x 82= 2 vendors out
. **Simply trim as follows:
. *(drop bus_exit, makes no sense b/cx 129/130)
. foreach x of varlist nonmmtotamt_cust_t1 totamt_cust_t1  {
  2.         preserve
  3.                 display "`x'"
  4.                 gen itemA= `x' if trt2==1 & attempts<=4 
  5.                 egen iranklo_Aa =rank(itemA) if trt2==1, unique //from ab
> ove
  6.                 egen iranklo_Ab =rank(-itemA) if trt2==1, unique //from b
> elow
  7.                 gen yupperA= `x'
  8.                 replace yupperA=. if (trt2==1 & iranklo_Aa<=2) | (trt2==1
>  & trt2>4) //trim differences within 3 attempts and cut off all above 3-atte
> mpts
  9.                 gen ylowerA= `x'
 10.                 replace ylowerA=. if (trt2==1 & iranklo_Ab<=2) | (trt2==1
>  & trt2>4)
 11.                 reg ylowerA  trt2, r
 12.                 reg yupperA trt2, r
 13.         restore
 14. } 
nonmmtotamt_cust_t1
(106 missing values generated)
(106 missing values generated)
(106 missing values generated)
(23 missing values generated)
(2 real changes made, 2 to missing)
(23 missing values generated)
(2 real changes made, 2 to missing)

Linear regression                               Number of obs     =        105
                                                F(1, 103)         =       0.01
                                                Prob > F          =     0.9341
                                                R-squared         =     0.0001
                                                Root MSE          =     266.79

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt2 |    4.66358   56.28883     0.08   0.934     -106.972    116.2992
       _cons |   180.0864   30.72059     5.86   0.000     119.1594    241.0135
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        105
                                                F(1, 103)         =       1.86
                                                Prob > F          =     0.1752
                                                R-squared         =     0.0252
                                                Root MSE          =     305.35

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt2 |   115.7469   84.79639     1.36   0.175    -52.42674    283.9206
       _cons |   180.0864   30.72059     5.86   0.000     119.1594    241.0135
------------------------------------------------------------------------------
totamt_cust_t1
(106 missing values generated)
(106 missing values generated)
(106 missing values generated)
(23 missing values generated)
(2 real changes made, 2 to missing)
(23 missing values generated)
(2 real changes made, 2 to missing)

Linear regression                               Number of obs     =        105
                                                F(1, 103)         =       1.26
                                                Prob > F          =     0.2641
                                                R-squared         =     0.0103
                                                Root MSE          =     1052.2

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt2 |   253.2238   225.5226     1.12   0.264    -194.0471    700.4946
       _cons |   1027.568     120.53     8.53   0.000     788.5252    1266.611
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        105
                                                F(1, 103)         =       5.07
                                                Prob > F          =     0.0265
                                                R-squared         =     0.0517
                                                Root MSE          =     1110.2

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt2 |   611.2238   271.5404     2.25   0.027     72.68735     1149.76
       _cons |   1027.568     120.53     8.53   0.000     788.5252    1266.611
------------------------------------------------------------------------------

. *
. foreach x of varlist nonmmtotamt_cust_t1 totamt_cust_t1  {
  2.         preserve
  3.                 display "`x'"
  4.                 gen itemA= `x' if trt3==1 & attempts<=4 
  5.                 egen iranklo_Aa =rank(itemA) if trt3==1, unique //from ab
> ove
  6.                 egen iranklo_Ab =rank(-itemA) if trt3==1, unique //from b
> elow
  7.                 gen yupperA= `x'
  8.                 replace yupperA=. if (trt3==1 & iranklo_Aa<=2) | (trt3==1
>  & trt3>4) //trim differences within 3 attempts and cut off all above 3-atte
> mpts
  9.                 gen ylowerA= `x'
 10.                 replace ylowerA=. if (trt3==1 & iranklo_Ab<=2) | (trt3==1
>  & trt3>4)
 11.                 reg ylowerA  trt3, r
 12.                 reg yupperA trt3, r
 13.         restore
 14. } 
nonmmtotamt_cust_t1
(103 missing values generated)
(103 missing values generated)
(103 missing values generated)
(23 missing values generated)
(2 real changes made, 2 to missing)
(23 missing values generated)
(2 real changes made, 2 to missing)

Linear regression                               Number of obs     =        105
                                                F(1, 103)         =       4.97
                                                Prob > F          =     0.0279
                                                R-squared         =     0.0219
                                                Root MSE          =     300.96

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt3 |  -103.4211   46.37593    -2.23   0.028    -195.3969   -11.44541
       _cons |   220.1519   38.04895     5.79   0.000     144.6908     295.613
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        105
                                                F(1, 103)         =       1.06
                                                Prob > F          =     0.3056
                                                R-squared         =     0.0060
                                                Root MSE          =     308.34

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt3 |  -54.95959   53.37853    -1.03   0.306    -160.8233    50.90414
       _cons |   220.1519   38.04895     5.79   0.000     144.6908     295.613
------------------------------------------------------------------------------
totamt_cust_t1
(103 missing values generated)
(103 missing values generated)
(103 missing values generated)
(23 missing values generated)
(2 real changes made, 2 to missing)
(23 missing values generated)
(2 real changes made, 2 to missing)

Linear regression                               Number of obs     =        105
                                                F(1, 103)         =       1.84
                                                Prob > F          =     0.1774
                                                R-squared         =     0.0166
                                                Root MSE          =     1062.1

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt3 |  -316.2108   232.8225    -1.36   0.177    -777.9594    145.5378
       _cons |   1166.557   121.2464     9.62   0.000     926.0934    1407.021
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        105
                                                F(1, 103)         =       0.00
                                                Prob > F          =     0.9919
                                                R-squared         =     0.0000
                                                Root MSE          =       1140

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt3 |   2.904576   284.3541     0.01   0.992    -561.0447    566.8539
       _cons |   1166.557   121.2464     9.62   0.000     926.0934    1407.021
------------------------------------------------------------------------------

. *
. foreach x of varlist nonmmtotamt_cust_t1 totamt_cust_t1  {
  2.         preserve
  3.                 display "`x'"
  4.                 gen itemA= `x' if trt4==1 & attempts<=4 
  5.                 egen iranklo_Aa =rank(itemA) if trt4==1, unique //from ab
> ove
  6.                 egen iranklo_Ab =rank(-itemA) if trt4==1, unique //from b
> elow
  7.                 gen yupperA= `x'
  8.                 replace yupperA=. if (trt4==1 & iranklo_Aa<=2) | (trt4==1
>  & trt4>4) //trim differences within 3 attempts and cut off all above 3-atte
> mpts
  9.                 gen ylowerA= `x'
 10.                 replace ylowerA=. if (trt4==1 & iranklo_Ab<=2) | (trt4==1
>  & trt4>4)
 11.                 reg ylowerA  trt4, r
 12.                 reg yupperA trt4, r
 13.         restore
 14. } 
nonmmtotamt_cust_t1
(104 missing values generated)
(104 missing values generated)
(104 missing values generated)
(23 missing values generated)
(2 real changes made, 2 to missing)
(23 missing values generated)
(2 real changes made, 2 to missing)

Linear regression                               Number of obs     =        105
                                                F(1, 103)         =       0.18
                                                Prob > F          =     0.6680
                                                R-squared         =     0.0016
                                                Root MSE          =     266.58

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt4 |  -24.48442   56.92682    -0.43   0.668    -137.3853    88.41649
       _cons |   187.2152   30.77658     6.08   0.000     126.1771    248.2533
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        105
                                                F(1, 103)         =       0.89
                                                Prob > F          =     0.3489
                                                R-squared         =     0.0121
                                                Root MSE          =     307.39

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt4 |   78.05404    82.9403     0.94   0.349    -86.43848    242.5466
       _cons |   187.2152   30.77658     6.08   0.000     126.1771    248.2533
------------------------------------------------------------------------------
totamt_cust_t1
(104 missing values generated)
(104 missing values generated)
(104 missing values generated)
(23 missing values generated)
(2 real changes made, 2 to missing)
(23 missing values generated)
(2 real changes made, 2 to missing)

Linear regression                               Number of obs     =        105
                                                F(1, 103)         =       0.57
                                                Prob > F          =     0.4532
                                                R-squared         =     0.0046
                                                Root MSE          =     1119.9

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt4 |  -174.7167   232.0649    -0.75   0.453    -634.9625    285.5292
       _cons |   1154.101   130.8434     8.82   0.000     894.6043    1413.598
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        105
                                                F(1, 103)         =       0.05
                                                Prob > F          =     0.8297
                                                R-squared         =     0.0004
                                                Root MSE          =     1139.8

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        trt4 |   53.20643   246.6934     0.22   0.830    -436.0517    542.4646
       _cons |   1154.101   130.8434     8.82   0.000     894.6043    1413.598
------------------------------------------------------------------------------

. *
. 
. 
. 
. ** Table C16 ---------------------------------------------------------------
> ------
. **No Marketing Effects: # of customers
. *extensive margin - no effect
. sum v1a1 if trtment==0 & dropouts==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
        v1a1 |         25       57.44    57.59129          0        200

. regress v1a1 m2q4a i.districtID mage mmarried makan mselfemployed m2q1a i.m3
> q1 trtment, robust //no effect

Linear regression                               Number of obs     =        107
                                                F(15, 91)         =       1.85
                                                Prob > F          =     0.0398
                                                R-squared         =     0.3141
                                                Root MSE          =     57.041

------------------------------------------------------------------------------
             |               Robust
        v1a1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       m2q4a |   .3506745   .1399931     2.50   0.014     .0725953    .6287537
             |
  districtID |
          3  |   8.008289   16.90104     0.47   0.637    -25.56354    41.58012
          4  |   25.90734   13.22114     1.96   0.053    -.3548432    52.16951
          5  |   129.8532   58.71322     2.21   0.029     13.22661    246.4798
          6  |    22.3864   14.96538     1.50   0.138    -7.340484    52.11329
          7  |   42.41871   19.28815     2.20   0.030     4.105165    80.73225
          8  |   15.87527   16.52152     0.96   0.339     -16.9427    48.69324
          9  |   27.02116   15.40105     1.75   0.083     -3.57114    57.61346
             |
        mage |   .2541598    1.00479     0.25   0.801    -1.741732    2.250052
    mmarried |   29.00784   21.42468     1.35   0.179    -13.54966    71.56535
       makan |   .0497037   13.24234     0.00   0.997    -26.25458    26.35398
mselfemplo~d |  -7.276795   15.31452    -0.48   0.636    -37.69721    23.14362
       m2q1a |   2.237173   2.824004     0.79   0.430    -3.372365     7.84671
      2.m3q1 |   22.09739   22.79464     0.97   0.335    -23.18135    67.37614
     trtment |   -3.42275   15.03681    -0.23   0.820    -33.29152    26.44602
       _cons |  -6.765694    36.3651    -0.19   0.853    -79.00049     65.4691
------------------------------------------------------------------------------

. *regress v1a1 mmtotamt_cust_t0 i.districtID mage mmarried makan mselfemploye
> d m2q1a i.m3q1 trtment, robust //no effect
. 
. regress v1a1 m2q4a i.districtID mage mmarried makan mselfemployed m2q1a i.m3
> q1 i.trt, robust //no effect

Linear regression                               Number of obs     =        107
                                                F(17, 89)         =       1.69
                                                Prob > F          =     0.0600
                                                R-squared         =     0.3165
                                                Root MSE          =     57.577

------------------------------------------------------------------------------
             |               Robust
        v1a1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       m2q4a |   .3473611   .1368391     2.54   0.013     .0754648    .6192574
             |
  districtID |
          3  |   8.323895   17.35874     0.48   0.633    -26.16756    42.81535
          4  |   25.42844   13.49948     1.88   0.063    -1.394746    52.25163
          5  |   128.4899   57.85628     2.22   0.029     13.53073    243.4491
          6  |   21.06793   13.78873     1.53   0.130    -6.329982    48.46584
          7  |   41.64871   19.21438     2.17   0.033     3.470143    79.82728
          8  |   15.13277    16.0551     0.94   0.348    -16.76836     47.0339
          9  |   26.03274   15.86807     1.64   0.104     -5.49678    57.56225
             |
        mage |   .3021022   1.010801     0.30   0.766    -1.706337    2.310542
    mmarried |    28.1504   21.27598     1.32   0.189    -14.12452    70.42531
       makan |  -.3491906   13.64858    -0.03   0.980    -27.46864    26.77026
mselfemplo~d |  -7.298563   15.22935    -0.48   0.633    -37.55895    22.96183
       m2q1a |   2.129185   2.856272     0.75   0.458    -3.546167    7.804537
      2.m3q1 |   22.44979    22.5433     1.00   0.322    -22.34328    67.24285
             |
         trt |
          1  |  -4.160664   15.20457    -0.27   0.785    -34.37181    26.05049
          2  |  -7.711229   16.04512    -0.48   0.632    -39.59253    24.17008
          3  |   .9726436   20.17107     0.05   0.962    -39.10683    41.05212
             |
       _cons |  -6.465429   35.41202    -0.18   0.856    -76.82836     63.8975
------------------------------------------------------------------------------

. *regress v1a1 mmtotamt_cust_t0 i.districtID mage mmarried makan mselfemploye
> d m2q1a i.m3q1 i.trt, robust //no effect
. 
. 
. 
. 
.  
.  
.  
.  
.  
.  
. 
end of do-file

. do "$do_loc/Shocks_Mar.19.2023.do" // 2-ish minutes

. /*
> JPE2023-Annan
> y = shocks mitigation: financial resilience*
> Phone Surveys + Intensive Tracking: April 2020+
> 
> Input:
>         - FFPhone in 2020/CustomersData.dta
>         - data-Mgt/Stats?/Mkt_census_xtics_+_interventions_localized.dta
> Output:
>         - NA
> */
. 
. ***************
. use "$dta_loc_repl/02_final/Customer_+_Mktcensus_+_Interventions.dta", clear

. 
. gen districtID = ge01

. 
. **unmitigated shocks?
. gen udeath=(c21a==1) if _merge==3
(180 missing values generated)

. gen urevenue=(c21b==1) if _merge==3
(180 missing values generated)

. gen usickness=(c21c==1) if _merge==3
(180 missing values generated)

. gen uweather=(c21d==1) if _merge==3
(180 missing values generated)

. gen uprices=(c21e==1) if _merge==3
(180 missing values generated)

. gen ushocks=(c21f==1) if _merge==3
(180 missing values generated)

. 
. gen udeath_t0=(c6q1a==1) 

. gen urevenue_t0=(c6q1b==1)

. gen usickness_t0=(c6q1c==1)

. gen uweather_t0=(c6q1d==1) 

. gen uprices_t0=(c6q1e==1)

. gen ushocks_t0=(c6q1f==1)

. 
. gen ushocks_exp_t1 = (udeath==1 | urevenue==1 | usickness==1 | uweather==1 |
>  uprices==1 | ushocks==1)  if _merge==3
(180 missing values generated)

. gen ushocks_exp_t0 = (udeath_t0==1 | urevenue_t0==1 | usickness_t0==1 | uwea
> ther_t0==1 | uprices_t0==1 | ushocks_t0==1) if _merge==3
(180 missing values generated)

. 
. gen health_t1=(usickness==1 | ushocks==1) if _merge==3
(180 missing values generated)

. gen revenue_t1=(urevenue==1 | ushocks==1) if _merge==3
(180 missing values generated)

. gen hhexpense_t1= (ushocks==1) if _merge==3
(180 missing values generated)

. 
. 
. **midterm: poverty effects?
. **poverty rate, by locality etc? 100% Nat. Pov
. gen c_pov_likelihood_t0=c_pov_likelihood if _merge==3
(180 missing values generated)

. egen c_rScore_t1 = rowtotal(c11 - c20) if _merge==3
(180 missing values generated)

. gen c_pov_likelihood_t1 = 91.4 if (c_rScore_t1>=0 & c_rScore_t1<=9)
(989 missing values generated)

. replace c_pov_likelihood_t1 =75.9 if (c_rScore_t1>=10 & c_rScore_t1<=14)
(1 real change made)

. replace c_pov_likelihood_t1 =66.8 if (c_rScore_t1>=15 & c_rScore_t1<=19)
(1 real change made)

. replace c_pov_likelihood_t1 =63.8 if (c_rScore_t1>=20 & c_rScore_t1<=24)
(11 real changes made)

. replace c_pov_likelihood_t1 =53.3 if (c_rScore_t1>=25 & c_rScore_t1<=29)
(22 real changes made)

. replace c_pov_likelihood_t1 =40.2 if (c_rScore_t1>=30 & c_rScore_t1<=34)
(38 real changes made)

. replace c_pov_likelihood_t1 =29.0 if (c_rScore_t1>=35 & c_rScore_t1<=39)
(60 real changes made)

. replace c_pov_likelihood_t1 =19.6 if (c_rScore_t1>=40 & c_rScore_t1<=44)
(80 real changes made)

. replace c_pov_likelihood_t1 =11.7 if (c_rScore_t1>=45 & c_rScore_t1<=49)
(101 real changes made)

. replace c_pov_likelihood_t1 =7.2 if (c_rScore_t1>=50 & c_rScore_t1<=54)
(78 real changes made)

. replace c_pov_likelihood_t1 =4.3 if (c_rScore_t1>=55 & c_rScore_t1<=59)
(97 real changes made)

. replace c_pov_likelihood_t1 =2.2 if (c_rScore_t1>=60 & c_rScore_t1<=64)
(92 real changes made)

. replace c_pov_likelihood_t1 =1.1 if (c_rScore_t1>=65 & c_rScore_t1<=69)
(83 real changes made)

. replace c_pov_likelihood_t1 =0.8 if (c_rScore_t1>=70 & c_rScore_t1<=74)
(46 real changes made)

. replace c_pov_likelihood_t1 =0.3 if (c_rScore_t1>=75 & c_rScore_t1<=79)
(68 real changes made)

. replace c_pov_likelihood_t1 =0.0 if (c_rScore_t1>=80 & c_rScore_t1<=100)
(31 real changes made)

. sum c_pov_likelihood_t0 c_pov_likelihood_t1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
c_pov_like~0 |        810    12.19383    14.30185          0       66.8
c_pov_like~1 |        810    11.67333    14.84664          0       91.4

. 
. 
. ** Table 9+10 --------------------------------------------------------------
> -------
. sum ushocks_exp_t1 revenue_t1 health_t1 hhexpense_t1 c_pov_likelihood_t1 if 
> trtment==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
ushocks_ex~1 |        143    .8951049    .3074953          0          1
  revenue_t1 |        143    .7832168    .4135021          0          1
   health_t1 |        143    .5314685    .5007627          0          1
hhexpense_t1 |        143    .4195804    .4952249          0          1
c_pov_like~1 |        143    9.899301    13.43894          0       63.8

. regress ushocks_exp_t1 ushocks_exp_t0 i.districtID cfemale cage cmarried cak
> an cselfemployed cEducAny cselfIncome trtment if _merge==3, cluster(ge02) le
> vel(95)

Linear regression                               Number of obs     =        810
                                                F(17, 124)        =       5.63
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0844
                                                Root MSE          =     .35408

                                 (Std. err. adjusted for 125 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
ushocks_ex~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
ushocks_ex~0 |   .0190354   .0306585     0.62   0.536    -.0416464    .0797171
             |
  districtID |
          2  |   .1549236   .0555333     2.79   0.006     .0450077    .2648396
          3  |   .1454879   .0557901     2.61   0.010     .0350637    .2559122
          4  |   .1666093   .0540028     3.09   0.003     .0597226     .273496
          5  |   .1651654   .0651355     2.54   0.012     .0362439    .2940869
          6  |  -.0792781   .0873083    -0.91   0.366    -.2520856    .0935294
          7  |  -.3086382   .1066608    -2.89   0.005    -.5197499   -.0975265
          8  |  -.0429853   .0674222    -0.64   0.525    -.1764327    .0904621
          9  |   .0335883   .0549969     0.61   0.542     -.075266    .1424426
             |
     cfemale |   .0253563   .0283323     0.89   0.373    -.0307212    .0814338
        cage |   -.000138   .0010363    -0.13   0.894    -.0021891    .0019131
    cmarried |   .0078057   .0275223     0.28   0.777    -.0466687    .0622801
       cakan |   .0199548   .0298441     0.67   0.505     -.039115    .0790247
cselfemplo~d |   .0049893   .0296553     0.17   0.867    -.0537068    .0636854
    cEducAny |   .0069831   .0473532     0.15   0.883    -.0867421    .1007083
 cselfIncome |  -.0229965   .0201633    -1.14   0.256    -.0629053    .0169123
     trtment |  -.0686299   .0336667    -2.04   0.044    -.1352658   -.0019941
       _cons |   .8537877    .089781     9.51   0.000     .6760859    1.031489
------------------------------------------------------------------------------

. regress revenue_t1 urevenue_t0 i.districtID cfemale cage cmarried cakan csel
> femployed cEducAny cselfIncome trtment if _merge==3, cluster(ge02) level(95)

Linear regression                               Number of obs     =        810
                                                F(17, 124)        =       4.42
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0565
                                                Root MSE          =     .43999

                                 (Std. err. adjusted for 125 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
  revenue_t1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
 urevenue_t0 |  -.0243136   .0518741    -0.47   0.640    -.1269869    .0783597
             |
  districtID |
          2  |    .229317   .0548421     4.18   0.000     .1207692    .3378647
          3  |   .2442519   .0690088     3.54   0.001      .107664    .3808397
          4  |   .2032397   .0566008     3.59   0.000     .0912109    .3152685
          5  |   -.033538   .1031195    -0.33   0.746    -.2376404    .1705644
          6  |  -.0012078   .0895663    -0.01   0.989    -.1784847    .1760691
          7  |  -.2134424   .1103258    -1.93   0.055     -.431808    .0049233
          8  |   .0646128    .067249     0.96   0.339    -.0684919    .1977174
          9  |   .0030725   .0612049     0.05   0.960    -.1180692    .1242141
             |
     cfemale |   .0115339   .0328303     0.35   0.726    -.0534465    .0765144
        cage |   -.000678   .0010724    -0.63   0.528    -.0028005    .0014446
    cmarried |  -.0280372   .0314576    -0.89   0.375    -.0903006    .0342262
       cakan |   .0266019   .0418304     0.64   0.526    -.0561922     .109396
cselfemplo~d |   .0465108   .0352739     1.32   0.190    -.0233061    .1163277
    cEducAny |   .0117873   .0601168     0.20   0.845    -.1072008    .1307753
 cselfIncome |  -.0165442   .0257687    -0.64   0.522    -.0675478    .0344593
     trtment |   -.072149   .0488614    -1.48   0.142    -.1688594    .0245614
       _cons |    .736951    .111209     6.63   0.000     .5168373    .9570647
------------------------------------------------------------------------------

. regress health_t1 usickness_t0 i.districtID cfemale cage cmarried cakan csel
> femployed cEducAny cselfIncome trtment if _merge==3, cluster(ge02) level(95)

Linear regression                               Number of obs     =        810
                                                F(17, 124)        =       8.25
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1607
                                                Root MSE          =     .46304

                                 (Std. err. adjusted for 125 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
   health_t1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
usickness_t0 |  -.0392514   .0420778    -0.93   0.353    -.1225352    .0440324
             |
  districtID |
          2  |   .0993705   .1131189     0.88   0.381    -.1245234    .3232644
          3  |   .0275726   .1217512     0.23   0.821    -.2134072    .2685524
          4  |    .109242   .0756327     1.44   0.151    -.0404563    .2589404
          5  |   .5735842   .0917267     6.25   0.000     .3920314    .7551371
          6  |  -.1507951   .0956135    -1.58   0.117     -.340041    .0384507
          7  |  -.0917892   .1361626    -0.67   0.501    -.3612931    .1777147
          8  |   .0289756   .0894578     0.32   0.747    -.1480865    .2060378
          9  |   .3791028   .0680718     5.57   0.000     .2443696     .513836
             |
     cfemale |  -.0291354   .0355009    -0.82   0.413    -.0994016    .0411307
        cage |   .0023615   .0013194     1.79   0.076    -.0002501     .004973
    cmarried |   .0108765   .0338909     0.32   0.749    -.0562031    .0779561
       cakan |  -.0439352   .0391984    -1.12   0.265    -.1215199    .0336495
cselfemplo~d |  -.0134724   .0369402    -0.36   0.716    -.0865875    .0596427
    cEducAny |  -.0195487   .0549332    -0.36   0.723     -.128277    .0891795
 cselfIncome |  -.0492482   .0211514    -2.33   0.022    -.0911126   -.0073837
     trtment |  -.0610614   .0617596    -0.99   0.325    -.1833009    .0611782
       _cons |   .4517546   .1277998     3.53   0.001      .198803    .7047061
------------------------------------------------------------------------------

. regress hhexpense_t1 ushocks_t0 i.districtID cfemale cage cmarried cakan cse
> lfemployed cEducAny cselfIncome trtment if _merge==3, cluster(ge02) level(95
> )

Linear regression                               Number of obs     =        810
                                                F(17, 124)        =       5.60
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1421
                                                Root MSE          =     .45296

                                 (Std. err. adjusted for 125 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
hhexpense_t1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  ushocks_t0 |    .030967   .0593181     0.52   0.603    -.0864401    .1483741
             |
  districtID |
          2  |  -.0300679   .0992719    -0.30   0.762    -.2265549     .166419
          3  |  -.1129518   .0867753    -1.30   0.195    -.2847045    .0588009
          4  |  -.0303884   .0816225    -0.37   0.710    -.1919422    .1311655
          5  |   .3535331   .1104969     3.20   0.002     .1348287    .5722374
          6  |  -.1343249   .0758306    -1.77   0.079    -.2844149     .015765
          7  |  -.0213167   .1354198    -0.16   0.875    -.2893503     .246717
          8  |   .0607723   .0834996     0.73   0.468    -.1044968    .2260413
          9  |    .356228   .0751051     4.74   0.000     .2075741     .504882
             |
     cfemale |  -.0386617   .0338804    -1.14   0.256    -.1057205     .028397
        cage |   .0013029   .0012393     1.05   0.295      -.00115    .0037558
    cmarried |    .019427   .0347423     0.56   0.577    -.0493377    .0881917
       cakan |   -.019029   .0388663    -0.49   0.625    -.0959563    .0578982
cselfemplo~d |   .0281505   .0362862     0.78   0.439    -.0436701     .099971
    cEducAny |   .0125836   .0639898     0.20   0.844      -.11407    .1392373
 cselfIncome |  -.0240873   .0213643    -1.13   0.262    -.0663732    .0181986
     trtment |  -.1045387   .0591516    -1.77   0.080    -.2216163    .0125389
       _cons |   .3038545    .131875     2.30   0.023     .0428368    .5648721
------------------------------------------------------------------------------

. regress c_pov_likelihood_t1 c_pov_likelihood_t0 i.districtID cfemale cage cm
> arried cakan cselfemployed cEducAny cselfIncome trtment if _merge==3, cluste
> r(ge02) level(95)

Linear regression                               Number of obs     =        810
                                                F(17, 124)        =       7.80
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1091
                                                Root MSE          =     14.163

                                 (Std. err. adjusted for 125 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
c_pov_like~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
c_pov_like~0 |   .2294165   .0494107     4.64   0.000     .1316188    .3272142
             |
  districtID |
          2  |   .7526372   2.758517     0.27   0.785     -4.70724    6.212514
          3  |   .5143498   3.476639     0.15   0.883    -6.366894    7.395593
          4  |   1.990463   1.893394     1.05   0.295    -1.757094    5.738021
          5  |  -4.220812   1.708249    -2.47   0.015    -7.601916   -.8397087
          6  |  -4.059885   1.951795    -2.08   0.040    -7.923034   -.1967357
          7  |  -7.534069   1.850812    -4.07   0.000    -11.19734   -3.870793
          8  |  -.5369049   1.747813    -0.31   0.759    -3.996317    2.922508
          9  |   2.093399   1.593508     1.31   0.191    -1.060601    5.247399
             |
     cfemale |    .976135   1.141951     0.85   0.394    -1.284106    3.236376
        cage |    .044098   .0381984     1.15   0.251    -.0315074    .1197034
    cmarried |  -.7149303   1.094575    -0.65   0.515    -2.881401     1.45154
       cakan |  -1.852801   1.220406    -1.52   0.132    -4.268326     .562724
cselfemplo~d |   .5104463   1.274185     0.40   0.689    -2.011524    3.032416
    cEducAny |  -2.890856    1.81488    -1.59   0.114    -6.483012    .7012998
 cselfIncome |  -.1751186   .5667705    -0.31   0.758    -1.296916    .9466791
     trtment |   1.161501   1.355405     0.86   0.393    -1.521225    3.844228
       _cons |   9.333556   2.925744     3.19   0.002     3.542689    15.12442
------------------------------------------------------------------------------

. 
. 
. tab trt, gen(trt)

        trt |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        185       18.69       18.69
          1 |        272       27.47       46.16
          2 |        257       25.96       72.12
          3 |        276       27.88      100.00
------------+-----------------------------------
      Total |        990      100.00

. regress ushocks_exp_t1 ushocks_exp_t0 i.districtID cfemale cage cmarried cak
> an cselfemployed cEducAny cselfIncome trt2 trt3 trt4 if _merge==3, cluster(g
> e02) level(95)

Linear regression                               Number of obs     =        810
                                                F(19, 124)        =       5.87
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0922
                                                Root MSE          =     .35303

                                 (Std. err. adjusted for 125 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
ushocks_ex~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
ushocks_ex~0 |   .0228449   .0300835     0.76   0.449    -.0366987    .0823885
             |
  districtID |
          2  |   .1551362   .0571246     2.72   0.008     .0420706    .2682017
          3  |   .1484415   .0525578     2.82   0.006     .0444149    .2524681
          4  |    .175858   .0557782     3.15   0.002     .0654574    .2862586
          5  |   .1680561   .0661864     2.54   0.012     .0370547    .2990575
          6  |  -.0916706   .0849252    -1.08   0.282    -.2597614    .0764202
          7  |  -.3105916   .1041652    -2.98   0.003    -.5167636   -.1044196
          8  |  -.0401128   .0674778    -0.59   0.553    -.1736703    .0934447
          9  |   .0290825   .0534865     0.54   0.588    -.0767823    .1349473
             |
     cfemale |   .0267465   .0279686     0.96   0.341    -.0286113    .0821043
        cage |  -.0001509   .0010509    -0.14   0.886    -.0022309    .0019292
    cmarried |   .0096694   .0277316     0.35   0.728    -.0452192     .064558
       cakan |   .0185262   .0300969     0.62   0.539     -.041044    .0780965
cselfemplo~d |   .0041432   .0298225     0.14   0.890    -.0548839    .0631704
    cEducAny |   .0141069   .0479157     0.29   0.769    -.0807317    .1089454
 cselfIncome |  -.0219195    .019832    -1.11   0.271    -.0611726    .0173337
        trt2 |  -.0990671   .0391297    -2.53   0.013    -.1765157   -.0216186
        trt3 |  -.0152752   .0386574    -0.40   0.693    -.0917891    .0612386
        trt4 |  -.0854007   .0451458    -1.89   0.061    -.1747568    .0039555
       _cons |   .8444107   .0906241     9.32   0.000     .6650402    1.023781
------------------------------------------------------------------------------

. test _b[trt2]=_b[trt4]

 ( 1)  trt2 - trt4 = 0

       F(  1,   124) =    0.10
            Prob > F =    0.7572

. test _b[trt3]=_b[trt4]

 ( 1)  trt3 - trt4 = 0

       F(  1,   124) =    2.68
            Prob > F =    0.1043

. test _b[trt2]=_b[trt3]

 ( 1)  trt2 - trt3 = 0

       F(  1,   124) =    4.97
            Prob > F =    0.0276

. test _b[trt2] + _b[trt3] =_b[trt4]

 ( 1)  trt2 + trt3 - trt4 = 0

       F(  1,   124) =    0.25
            Prob > F =    0.6183

. regress revenue_t1 urevenue_t0 i.districtID cfemale cage cmarried cakan csel
> femployed cEducAny cselfIncome trt2 trt3 trt4 if _merge==3, cluster(ge02) le
> vel(95)

Linear regression                               Number of obs     =        810
                                                F(19, 124)        =       4.06
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0655
                                                Root MSE          =     .43844

                                 (Std. err. adjusted for 125 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
  revenue_t1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
 urevenue_t0 |  -.0288966   .0501218    -0.58   0.565    -.1281017    .0703085
             |
  districtID |
          2  |   .2308715   .0593162     3.89   0.000     .1134681    .3482749
          3  |   .2466103   .0609966     4.04   0.000     .1258808    .3673397
          4  |    .214613   .0604019     3.55   0.001     .0950608    .3341653
          5  |  -.0340984    .107224    -0.32   0.751    -.2463247     .178128
          6  |  -.0170736    .087254    -0.20   0.845    -.1897737    .1556265
          7  |   -.215953   .1055326    -2.05   0.043    -.4248315   -.0070744
          8  |   .0655565   .0654455     1.00   0.318    -.0639785    .1950915
          9  |  -.0040632   .0588269    -0.07   0.945    -.1204981    .1123717
             |
     cfemale |   .0129997   .0323196     0.40   0.688    -.0509698    .0769692
        cage |  -.0006924   .0011017    -0.63   0.531     -.002873    .0014882
    cmarried |  -.0254485   .0317701    -0.80   0.425    -.0883304    .0374335
       cakan |   .0252797   .0419058     0.60   0.547    -.0576636     .108223
cselfemplo~d |    .044333   .0356576     1.24   0.216    -.0262434    .1149094
    cEducAny |   .0212975    .061193     0.35   0.728    -.0998205    .1424156
 cselfIncome |   -.015286   .0252885    -0.60   0.547    -.0653391     .034767
        trt2 |   -.114675   .0579698    -1.98   0.050    -.2294134    .0000634
        trt3 |   -.002196   .0573083    -0.04   0.969    -.1156252    .1112331
        trt4 |  -.0912482   .0575038    -1.59   0.115    -.2050644    .0225679
       _cons |   .7293597   .1133765     6.43   0.000     .5049558    .9537636
------------------------------------------------------------------------------

. test _b[trt2]=_b[trt4]

 ( 1)  trt2 - trt4 = 0

       F(  1,   124) =    0.20
            Prob > F =    0.6580

. test _b[trt3]=_b[trt4]

 ( 1)  trt3 - trt4 = 0

       F(  1,   124) =    2.92
            Prob > F =    0.0900

. test _b[trt2]=_b[trt3]

 ( 1)  trt2 - trt3 = 0

       F(  1,   124) =    4.25
            Prob > F =    0.0414

. test _b[trt2] + _b[trt3] =_b[trt4]

 ( 1)  trt2 + trt3 - trt4 = 0

       F(  1,   124) =    0.11
            Prob > F =    0.7385

. regress health_t1 usickness_t0 i.districtID cfemale cage cmarried cakan csel
> femployed cEducAny cselfIncome trt2 trt3 trt4 if _merge==3, cluster(ge02) le
> vel(95)

Linear regression                               Number of obs     =        810
                                                F(19, 124)        =       8.55
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1676
                                                Root MSE          =     .46172

                                 (Std. err. adjusted for 125 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
   health_t1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
usickness_t0 |  -.0355511   .0420623    -0.85   0.400    -.1188043     .047702
             |
  districtID |
          2  |   .0995574   .1040691     0.96   0.341    -.1064245    .3055394
          3  |   .0284204   .1267449     0.22   0.823    -.2224432     .279284
          4  |   .1182508   .0745037     1.59   0.115     -.029213    .2657145
          5  |   .5756131   .0830635     6.93   0.000     .4112071    .7400191
          6  |  -.1678511   .0981576    -1.71   0.090    -.3621324    .0264303
          7  |  -.0941809   .1265462    -0.74   0.458    -.3446513    .1562895
          8  |   .0298404   .0866564     0.34   0.731     -.141677    .2013577
          9  |   .3709955   .0648175     5.72   0.000     .2427036    .4992874
             |
     cfemale |  -.0275386   .0350856    -0.78   0.434    -.0969828    .0419056
        cage |   .0023423    .001346     1.74   0.084    -.0003218    .0050065
    cmarried |   .0138492    .033902     0.41   0.684    -.0532524    .0809509
       cakan |  -.0450825   .0393062    -1.15   0.254    -.1228804    .0327154
cselfemplo~d |   -.015331   .0371485    -0.41   0.681    -.0888583    .0581962
    cEducAny |  -.0106941   .0567688    -0.19   0.851    -.1230553    .1016672
 cselfIncome |  -.0482209   .0206393    -2.34   0.021    -.0890718     -.00737
        trt2 |  -.0930879   .0697802    -1.33   0.185    -.2312024    .0450266
        trt3 |   .0083589   .0722304     0.12   0.908    -.1346053    .1513231
        trt4 |  -.0908149   .0683069    -1.33   0.186    -.2260133    .0443835
       _cons |   .4438433   .1297167     3.42   0.001     .1870976    .7005891
------------------------------------------------------------------------------

. test _b[trt2]=_b[trt4]

 ( 1)  trt2 - trt4 = 0

       F(  1,   124) =    0.00
            Prob > F =    0.9676

. test _b[trt3]=_b[trt4]

 ( 1)  trt3 - trt4 = 0

       F(  1,   124) =    2.91
            Prob > F =    0.0906

. test _b[trt2]=_b[trt3]

 ( 1)  trt2 - trt3 = 0

       F(  1,   124) =    2.63
            Prob > F =    0.1074

. test _b[trt2] + _b[trt3] =_b[trt4]

 ( 1)  trt2 + trt3 - trt4 = 0

       F(  1,   124) =    0.00
            Prob > F =    0.9459

. regress hhexpense_t1 ushocks_t0 i.districtID cfemale cage cmarried cakan cse
> lfemployed cEducAny cselfIncome trt2 trt3 trt4 if _merge==3, cluster(ge02) l
> evel(95)

Linear regression                               Number of obs     =        810
                                                F(19, 124)        =       5.57
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1492
                                                Root MSE          =     .45164

                                 (Std. err. adjusted for 125 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
hhexpense_t1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  ushocks_t0 |   .0339917   .0597057     0.57   0.570    -.0841827     .152166
             |
  districtID |
          2  |  -.0284822   .1019332    -0.28   0.780    -.2302365    .1732722
          3  |  -.1109142   .0955009    -1.16   0.248    -.2999373    .0781089
          4  |  -.0195265   .0781013    -0.25   0.803    -.1741109    .1350579
          5  |   .3558429   .1192037     2.99   0.003     .1199054    .5917804
          6  |  -.1490738   .0768989    -1.94   0.055    -.3012783    .0031306
          7  |  -.0214423   .1280008    -0.17   0.867    -.2747917     .231907
          8  |   .0624748   .0801738     0.78   0.437    -.0962116    .2211611
          9  |   .3501975   .0717302     4.88   0.000     .2082232    .4921717
             |
     cfemale |  -.0373214   .0332756    -1.12   0.264    -.1031832    .0285403
        cage |   .0012868   .0012823     1.00   0.318    -.0012512    .0038247
    cmarried |     .02228   .0348429     0.64   0.524    -.0466839    .0912438
       cakan |  -.0199939   .0382346    -0.52   0.602    -.0956708    .0556831
cselfemplo~d |    .026104   .0365199     0.71   0.476    -.0461792    .0983872
    cEducAny |   .0212583   .0648254     0.33   0.744    -.1070494     .149566
 cselfIncome |  -.0229615   .0206625    -1.11   0.269    -.0638583    .0179354
        trt2 |  -.1371197   .0677657    -2.02   0.045     -.271247   -.0029923
        trt3 |  -.0358822   .0759367    -0.47   0.637    -.1861822    .1144177
        trt4 |  -.1321065   .0646916    -2.04   0.043    -.2601493   -.0040636
       _cons |   .2951523   .1345897     2.19   0.030     .0287616     .561543
------------------------------------------------------------------------------

. test _b[trt2]=_b[trt4]

 ( 1)  trt2 - trt4 = 0

       F(  1,   124) =    0.01
            Prob > F =    0.9303

. test _b[trt3]=_b[trt4]

 ( 1)  trt3 - trt4 = 0

       F(  1,   124) =    2.15
            Prob > F =    0.1453

. test _b[trt2]=_b[trt3]

 ( 1)  trt2 - trt3 = 0

       F(  1,   124) =    2.10
            Prob > F =    0.1496

. test _b[trt2] + _b[trt3] =_b[trt4]

 ( 1)  trt2 + trt3 - trt4 = 0

       F(  1,   124) =    0.19
            Prob > F =    0.6651

. regress c_pov_likelihood_t1 c_pov_likelihood_t0 i.districtID cfemale cage cm
> arried cakan cselfemployed cEducAny cselfIncome trt2 trt3 trt4 if _merge==3,
>  cluster(ge02) level(95)

Linear regression                               Number of obs     =        810
                                                F(19, 124)        =       7.55
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1117
                                                Root MSE          =      14.16

                                 (Std. err. adjusted for 125 clusters in ge02)
------------------------------------------------------------------------------
             |               Robust
c_pov_like~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
c_pov_like~0 |   .2267706   .0488943     4.64   0.000     .1299952    .3235461
             |
  districtID |
          2  |   .8758729   2.633985     0.33   0.740    -4.337522    6.089267
          3  |   .4118165   3.336094     0.12   0.902    -6.191249    7.014882
          4  |   1.946015   1.944464     1.00   0.319    -1.902625    5.794654
          5  |  -4.069005   1.798179    -2.26   0.025    -7.628105   -.5099061
          6  |  -4.047887   1.999951    -2.02   0.045     -8.00635   -.0894242
          7  |  -7.380187   1.827618    -4.04   0.000    -10.99756   -3.762819
          8  |  -.5619007   1.763761    -0.32   0.751    -4.052878    2.929077
          9  |   2.024703   1.592064     1.27   0.206    -1.126437    5.175844
             |
     cfemale |   .9614443   1.139318     0.84   0.400    -1.293585    3.216474
        cage |   .0428526   .0382257     1.12   0.264    -.0328067    .1185119
    cmarried |  -.6208614    1.09362    -0.57   0.571    -2.785441    1.543719
       cakan |  -1.802247   1.226395    -1.47   0.144    -4.229626    .6251319
cselfemplo~d |   .4670552   1.253572     0.37   0.710    -2.014115    2.948225
    cEducAny |  -2.859782    1.81305    -1.58   0.117    -6.448317    .7287521
 cselfIncome |  -.1934896   .5554425    -0.35   0.728    -1.292866    .9058867
        trt2 |   1.755205   1.630816     1.08   0.284    -1.472636    4.983046
        trt3 |   1.824982   1.522767     1.20   0.233    -1.189002    4.838965
        trt4 |   .0010814   1.640047     0.00   0.999    -3.245031    3.247194
       _cons |   9.375855   2.931785     3.20   0.002     3.573033    15.17868
------------------------------------------------------------------------------

. test _b[trt2]=_b[trt4]

 ( 1)  trt2 - trt4 = 0

       F(  1,   124) =    1.27
            Prob > F =    0.2621

. test _b[trt3]=_b[trt4]

 ( 1)  trt3 - trt4 = 0

       F(  1,   124) =    1.59
            Prob > F =    0.2091

. test _b[trt2]=_b[trt3]

 ( 1)  trt2 - trt3 = 0

       F(  1,   124) =    0.00
            Prob > F =    0.9599

. test _b[trt2] + _b[trt3] =_b[trt4]

 ( 1)  trt2 + trt3 - trt4 = 0

       F(  1,   124) =    2.63
            Prob > F =    0.1076

. 
. 
. ** Table C10 ---------------------------------------------------------------
> ------
. *Robustness checks - Inference, Multiple Testing, Attrition, LASSO Estimatio
> n
. *POOLED
. ***wild cluster bootstrap, pval
. reg ushocks_exp_t1 ushocks_exp_t0 i.districtID cfemale cage cmarried cakan c
> selfemployed cEducAny cselfIncome trtment if _merge==3, cluster(loccode) lev
> el(95)

Linear regression                               Number of obs     =        810
                                                F(17, 124)        =       5.63
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0844
                                                Root MSE          =     .35408

                              (Std. err. adjusted for 125 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
ushocks_ex~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
ushocks_ex~0 |   .0190354   .0306585     0.62   0.536    -.0416464    .0797171
             |
  districtID |
          2  |   .1549236   .0555333     2.79   0.006     .0450077    .2648396
          3  |   .1454879   .0557901     2.61   0.010     .0350637    .2559122
          4  |   .1666093   .0540028     3.09   0.003     .0597226     .273496
          5  |   .1651654   .0651355     2.54   0.012     .0362439    .2940869
          6  |  -.0792781   .0873083    -0.91   0.366    -.2520856    .0935294
          7  |  -.3086382   .1066608    -2.89   0.005    -.5197499   -.0975265
          8  |  -.0429853   .0674222    -0.64   0.525    -.1764327    .0904621
          9  |   .0335883   .0549969     0.61   0.542     -.075266    .1424426
             |
     cfemale |   .0253563   .0283323     0.89   0.373    -.0307212    .0814338
        cage |   -.000138   .0010363    -0.13   0.894    -.0021891    .0019131
    cmarried |   .0078057   .0275223     0.28   0.777    -.0466687    .0622801
       cakan |   .0199548   .0298441     0.67   0.505     -.039115    .0790247
cselfemplo~d |   .0049893   .0296553     0.17   0.867    -.0537068    .0636854
    cEducAny |   .0069831   .0473532     0.15   0.883    -.0867421    .1007083
 cselfIncome |  -.0229965   .0201633    -1.14   0.256    -.0629053    .0169123
     trtment |  -.0686299   .0336667    -2.04   0.044    -.1352658   -.0019941
       _cons |   .8537877    .089781     9.51   0.000     .6760859    1.031489
------------------------------------------------------------------------------

. boottest trt, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by loccode, Rademacher weights:
  trt

                          t(124) =    -2.0385
                        Prob>|t| =     0.0620

95% confidence set for null hypothesis expression: [−.1406, .005935]

. **randomization inf: permuntation test, pval
. ritest trtment _b[trtment], reps($bootstrap_reps) cluster(loccode) strata(di
> strictID) seed(546): reg ushocks_exp_t1 ushocks_exp_t0 i.districtID cfemale 
> cage cmarried cakan cselfemployed cEducAny cselfIncome trtment
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       810
-------------+----------------------------------   F(17, 792)      =      4.30
       Model |  9.15901544        17  .538765614   Prob > F        =    0.0000
    Residual |  99.2965401       792  .125374419   R-squared       =    0.0844
-------------+----------------------------------   Adj R-squared   =    0.0648
       Total |  108.455556       809  .134061255   Root MSE        =    .35408

------------------------------------------------------------------------------
ushocks_ex~1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
ushocks_ex~0 |   .0190354   .0285463     0.67   0.505        -.037    .0750708
             |
  districtID |
          2  |   .1549236   .0618805     2.50   0.012     .0334544    .2763929
          3  |   .1454879   .0792665     1.84   0.067    -.0101093    .3010851
          4  |   .1666093   .0493027     3.38   0.001     .0698298    .2633888
          5  |   .1651654   .0761674     2.17   0.030     .0156516    .3146791
          6  |  -.0792781     .06253    -1.27   0.205    -.2020222     .043466
          7  |  -.3086382   .0782826    -3.94   0.000    -.4623042   -.1549722
          8  |  -.0429853   .0468434    -0.92   0.359    -.1349372    .0489666
          9  |   .0335883   .0385418     0.87   0.384     -.042068    .1092445
             |
     cfemale |   .0253563   .0267289     0.95   0.343    -.0271115    .0778241
        cage |   -.000138   .0008903    -0.16   0.877    -.0018857    .0016096
    cmarried |   .0078057   .0271539     0.29   0.774    -.0454964    .0611079
       cakan |   .0199548   .0287091     0.70   0.487    -.0364001    .0763097
cselfemplo~d |   .0049893   .0292802     0.17   0.865    -.0524868    .0624653
    cEducAny |   .0069831   .0430456     0.16   0.871    -.0775139    .0914802
 cselfIncome |  -.0229965   .0180035    -1.28   0.202    -.0583368    .0123438
     trtment |  -.0686299   .0330907    -2.07   0.038    -.1335858   -.0036741
       _cons |   .8537877   .0809096    10.55   0.000     .6949651     1.01261
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000

      Command: regress ushocks_exp_t1 ushocks_exp_t0 i.districtID cfemale
                   cage cmarried cakan cselfemployed cEducAny cselfIncome
                   trtment
        _pm_1: _b[trtment]
  res. var(s):  trtment
   Resampling:  Permuting trtment
Clust. var(s):  loccode
     Clusters:  130
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |  -.0686299      84    1000  0.0840  0.0088  .0675527    .102943
------------------------------------------------------------------------------
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. **mht: implement Romano-Wolf (2005) procedure, pval
. rwolf ushocks_exp_t1 revenue_t1 health_t1 hhexpense_t1 c_pov_likelihood_t1, 
> indepvar(trtment trt2 trt3 trt4) reps($bootstrap_reps) seed(124) controls(i.
> districtID cfemale cage cmarried cakan cselfemployed cEducAny cselfIncome) /
> /family (all 4 0-1 shocks, poverty %)
Bootstrap replications (1000). This may take some time.
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..................................................     50
..................................................     100
..................................................     150
..................................................     200
..................................................     250
..................................................     300
..................................................     350
..................................................     400
..................................................     450
..................................................     500
..................................................     550
..................................................     600
..................................................     650
..................................................     700
..................................................     750
..................................................     800
..................................................     850
..................................................     900
..................................................     950
..................................................     1000




Romano-Wolf step-down adjusted p-values


Independent variable:  trtment
Outcome variables:   ushocks_exp_t1 revenue_t1 health_t1 hhexpense_t1
                     c_pov_likelihood_t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
     ushocks_exp_t1 |     0.0290             0.0160              0.0739
         revenue_t1 |     0.0526             0.0480              0.1119
          health_t1 |     0.0567             0.0450              0.1119
       hhexpense_t1 |     0.0063             0.0050              0.0200
c_pov_likelihood_t1 |     0.8656             0.8551              0.8551
------------------------------------------------------------------------------


Independent variable:  trt2
Outcome variables:   ushocks_exp_t1 revenue_t1 health_t1 hhexpense_t1
                     c_pov_likelihood_t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
     ushocks_exp_t1 |     0.6211             0.6364              0.9331
         revenue_t1 |     0.5849             0.5664              0.9331
          health_t1 |     0.9807             0.9790              0.9840
       hhexpense_t1 |     0.8974             0.8771              0.9840
c_pov_likelihood_t1 |     0.0930             0.1079              0.3247
------------------------------------------------------------------------------


Independent variable:  trt3
Outcome variables:   ushocks_exp_t1 revenue_t1 health_t1 hhexpense_t1
                     c_pov_likelihood_t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
     ushocks_exp_t1 |     0.0496             0.0519              0.0989
         revenue_t1 |     0.0362             0.0430              0.0989
          health_t1 |     0.0224             0.0200              0.0849
       hhexpense_t1 |     0.0290             0.0280              0.0879
c_pov_likelihood_t1 |     0.1711             0.1868              0.1868
------------------------------------------------------------------------------


Independent variable:  trt4
Outcome variables:   ushocks_exp_t1 revenue_t1 health_t1 hhexpense_t1
                     c_pov_likelihood_t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
     ushocks_exp_t1 |          .             0.0010              0.0010
         revenue_t1 |          .             0.0010              0.0010
          health_t1 |          .             0.0010              0.0010
       hhexpense_t1 |          .             0.0010              0.0010
c_pov_likelihood_t1 |          .             0.0010              0.0010
------------------------------------------------------------------------------



. **attrition bounds
. **1. [Lee Bounds]**
. leebounds ushocks_exp_t1 trtment, level(95) cieffect tight()

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0671
Effect 95% conf. interval          : [-0.1297  0.0739]

------------------------------------------------------------------------------
ushocks_ex~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trtment      |
       lower |   -.078313   .0311004    -2.52   0.012    -.1392687   -.0173573
       upper |  -.0063849   .0486029    -0.13   0.895    -.1016448     .088875
------------------------------------------------------------------------------

. **2. [Behajel et al. Bounds]**
. gen attempts= c0a
(180 missing values generated)

. bys trtment: tab attempts

------------------------------------------------------------------------------
-> trtment = 0

   attempts |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        100       69.93       69.93
          2 |         26       18.18       88.11
          3 |          6        4.20       92.31
          4 |          4        2.80       95.10
          5 |          3        2.10       97.20
          6 |          2        1.40       98.60
          9 |          1        0.70       99.30
         11 |          1        0.70      100.00
------------+-----------------------------------
      Total |        143      100.00

------------------------------------------------------------------------------
-> trtment = 1

   attempts |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        454       68.07       68.07
          2 |        119       17.84       85.91
          3 |         63        9.45       95.35
          4 |         13        1.95       97.30
          5 |         14        2.10       99.40
          7 |          2        0.30       99.70
          8 |          2        0.30      100.00
------------+-----------------------------------
      Total |        667      100.00


. **with 3 or less phone /contact attempts: ctr has 92% response rate, trt has
>  95% response rate
. **use number of attempts - "effort" to rank & bound te
. **so trim (95-92)/95 =3% of trt group, x 667= 20 customers out
. **Simply trim as follows:
. foreach x of varlist ushocks_exp_t1 {
  2.         preserve
  3.                 display "`x'"
  4.                 gen itemA= `x' if trtment==1 & attempts<=3 
  5.                 egen iranklo_Aa =rank(itemA) if trtment==1, unique //from
>  above
  6.                 egen iranklo_Ab =rank(-itemA) if trtment==1, unique //fro
> m below
  7.                 gen yupperA= `x'
  8.                 replace yupperA=. if (trtment==1 & iranklo_Aa<=20) | (trt
> ment==1 & attempts>3) //trim differences within 3 attempts and cut off all a
> bove 3-attempts
  9.                 gen ylowerA= `x'
 10.                 replace ylowerA=. if (trtment==1 & iranklo_Ab<=20) | (trt
> ment==1 & attempts>3)
 11.                 reg ylowerA  trtment, r
 12.                 reg yupperA trtment, r
 13.         restore
 14. } 
ushocks_exp_t1
(354 missing values generated)
(354 missing values generated)
(354 missing values generated)
(180 missing values generated)
(51 real changes made, 51 to missing)
(180 missing values generated)
(51 real changes made, 51 to missing)

Linear regression                               Number of obs     =        759
                                                F(1, 757)         =       6.05
                                                Prob > F          =     0.0141
                                                R-squared         =     0.0060
                                                Root MSE          =     .37027

------------------------------------------------------------------------------
             |               Robust
     ylowerA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     trtment |  -.0736763   .0299512    -2.46   0.014    -.1324736    -.014879
       _cons |   .8951049   .0256578    34.89   0.000      .844736    .9454738
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        759
                                                F(1, 757)         =       1.97
                                                Prob > F          =     0.1607
                                                R-squared         =     0.0022
                                                Root MSE          =     .34534

------------------------------------------------------------------------------
             |               Robust
     yupperA | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     trtment |  -.0412088   .0293494    -1.40   0.161    -.0988247    .0164071
       _cons |   .8951049   .0256578    34.89   0.000      .844736    .9454738
------------------------------------------------------------------------------

. *
. 
. *SEPARATE
. ***wild cluster bootstrap, pval
. reg ushocks_exp_t1 ushocks_exp_t0 i.districtID cfemale cage cmarried cakan c
> selfemployed cEducAny cselfIncome trt2 trt3 trt4 if _merge==3, cluster(locco
> de) level(95)

Linear regression                               Number of obs     =        810
                                                F(19, 124)        =       5.87
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0922
                                                Root MSE          =     .35303

                              (Std. err. adjusted for 125 clusters in loccode)
------------------------------------------------------------------------------
             |               Robust
ushocks_ex~1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
ushocks_ex~0 |   .0228449   .0300835     0.76   0.449    -.0366987    .0823885
             |
  districtID |
          2  |   .1551362   .0571246     2.72   0.008     .0420706    .2682017
          3  |   .1484415   .0525578     2.82   0.006     .0444149    .2524681
          4  |    .175858   .0557782     3.15   0.002     .0654574    .2862586
          5  |   .1680561   .0661864     2.54   0.012     .0370547    .2990575
          6  |  -.0916706   .0849252    -1.08   0.282    -.2597614    .0764202
          7  |  -.3105916   .1041652    -2.98   0.003    -.5167636   -.1044196
          8  |  -.0401128   .0674778    -0.59   0.553    -.1736703    .0934447
          9  |   .0290825   .0534865     0.54   0.588    -.0767823    .1349473
             |
     cfemale |   .0267465   .0279686     0.96   0.341    -.0286113    .0821043
        cage |  -.0001509   .0010509    -0.14   0.886    -.0022309    .0019292
    cmarried |   .0096694   .0277316     0.35   0.728    -.0452192     .064558
       cakan |   .0185262   .0300969     0.62   0.539     -.041044    .0780965
cselfemplo~d |   .0041432   .0298225     0.14   0.890    -.0548839    .0631704
    cEducAny |   .0141069   .0479157     0.29   0.769    -.0807317    .1089454
 cselfIncome |  -.0219195    .019832    -1.11   0.271    -.0611726    .0173337
        trt2 |  -.0990671   .0391297    -2.53   0.013    -.1765157   -.0216186
        trt3 |  -.0152752   .0386574    -0.40   0.693    -.0917891    .0612386
        trt4 |  -.0854007   .0451458    -1.89   0.061    -.1747568    .0039555
       _cons |   .8444107   .0906241     9.32   0.000     .6650402    1.023781
------------------------------------------------------------------------------

. boottest trt2, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by loccode, Rademacher weights:
  trt2

                          t(124) =    -2.5318
                        Prob>|t| =     0.0190

95% confidence set for null hypothesis expression: [−.1872, −.01526]

. boottest trt3, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by loccode, Rademacher weights:
  trt3

                          t(124) =    -0.3951
                        Prob>|t| =     0.7070

95% confidence set for null hypothesis expression: [−.09906, .06927]

. boottest trt4, rep($bootstrap_reps) level(95) nogr seed(15465)

Note: The bootstrap usually performs best when the confidence level (here, 95%
> )
      times the number of replications plus 1 (1000+1=1001) is an integer.

Wild bootstrap-t, null imposed, 1000 replications, Wald test, bootstrap cluste
> ring by loccode, Rademacher weights:
  trt4

                          t(124) =    -1.8917
                        Prob>|t| =     0.0850

95% confidence set for null hypothesis expression: [−.1848, .009738]

. **randomization inf: permuntation test, pval
. ritest trt2 trt3 trt4 _b[trt2] _b[trt3] _b[trt4], reps($bootstrap_reps) clus
> ter(loccode) strata(districtID) seed(546): reg ushocks_exp_t1 ushocks_exp_t0
>  i.districtID cfemale cage cmarried cakan cselfemployed cEducAny cselfIncome
>  trt2 trt3 trt4
(running regress on estimation sample)

      Source |       SS           df       MS      Number of obs   =       810
-------------+----------------------------------   F(19, 790)      =      4.22
       Model |  9.99643652        19  .526128238   Prob > F        =    0.0000
    Residual |   98.459119       790  .124631796   R-squared       =    0.0922
-------------+----------------------------------   Adj R-squared   =    0.0703
       Total |  108.455556       809  .134061255   Root MSE        =    .35303

------------------------------------------------------------------------------
ushocks_ex~1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
ushocks_ex~0 |   .0228449   .0286848     0.80   0.426    -.0334625    .0791522
             |
  districtID |
          2  |   .1551362   .0617339     2.51   0.012     .0339542    .2763181
          3  |   .1484415   .0790416     1.88   0.061    -.0067149    .3035978
          4  |    .175858   .0493073     3.57   0.000     .0790692    .2726468
          5  |   .1680561   .0761315     2.21   0.028      .018612    .3175001
          6  |  -.0916706   .0626099    -1.46   0.144     -.214572    .0312307
          7  |  -.3105916   .0781291    -3.98   0.000    -.4639567   -.1572264
          8  |  -.0401128   .0467376    -0.86   0.391    -.1318573    .0516318
          9  |   .0290825   .0384673     0.76   0.450    -.0464278    .1045928
             |
     cfemale |   .0267465   .0266562     1.00   0.316    -.0255789    .0790719
        cage |  -.0001509    .000888    -0.17   0.865     -.001894    .0015922
    cmarried |   .0096694   .0271065     0.36   0.721    -.0435399    .0628786
       cakan |   .0185262   .0286424     0.65   0.518    -.0376979    .0747504
cselfemplo~d |   .0041432   .0291954     0.14   0.887    -.0531664    .0614529
    cEducAny |   .0141069   .0430062     0.33   0.743    -.0703132    .0985269
 cselfIncome |  -.0219195   .0179568    -1.22   0.223    -.0571681    .0133291
        trt2 |  -.0990671   .0381103    -2.60   0.010    -.1738765   -.0242577
        trt3 |  -.0152752   .0390183    -0.39   0.696    -.0918671    .0613167
        trt4 |  -.0854007   .0381198    -2.24   0.025    -.1602287   -.0105726
       _cons |   .8444107    .080774    10.45   0.000     .6858536    1.002968
------------------------------------------------------------------------------

Resampling replications (1,000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
.................................................. 1,000
Warning: 100% of the resampled realizations for _pm_1 are exactly identical to
>  original value
Warning: 100% of the resampled realizations for _pm_2 are exactly identical to
>  original value

      Command: regress ushocks_exp_t1 ushocks_exp_t0 i.districtID cfemale
                   cage cmarried cakan cselfemployed cEducAny cselfIncome
                   trt2 trt3 trt4
        _pm_1: trt3
        _pm_2: trt4
        _pm_3: _b[trt2]
        _pm_4: _b[trt3]
        _pm_5: _b[trt4]
  res. var(s):  trt2
   Resampling:  Permuting trt2
Clust. var(s):  loccode
     Clusters:  130
Strata var(s):  districtID
       Strata:  9

------------------------------------------------------------------------------
T            |     T(obs)       c       n   p=c/n   SE(p) [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _pm_1 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_2 |          0    1000    1000  1.0000  0.0000  .9963179          1
       _pm_3 |  -.0990671       7    1000  0.0070  0.0026  .0028189   .0143692
       _pm_4 |  -.0152752    1000    1000  1.0000  0.0000  .9963179          1
       _pm_5 |  -.0854007       0    1000  0.0000  0.0000         0   .0036821
------------------------------------------------------------------------------
Note: Confidence intervals are with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}

. **mht: implement Romano-Wolf (2005) procedure, pval
. rwolf ushocks_exp_t1 revenue_t1 health_t1 hhexpense_t1 c_pov_likelihood_t1, 
> indepvar(trt2 trt3 trt4) reps($bootstrap_reps) seed(124) controls(i.district
> ID cfemale cage cmarried cakan cselfemployed cEducAny cselfIncome) //family 
> (all 4 0-1 shocks, poverty %)
Bootstrap replications (1000). This may take some time.
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..................................................     50
..................................................     100
..................................................     150
..................................................     200
..................................................     250
..................................................     300
..................................................     350
..................................................     400
..................................................     450
..................................................     500
..................................................     550
..................................................     600
..................................................     650
..................................................     700
..................................................     750
..................................................     800
..................................................     850
..................................................     900
..................................................     950
..................................................     1000




Romano-Wolf step-down adjusted p-values


Independent variable:  trt2
Outcome variables:   ushocks_exp_t1 revenue_t1 health_t1 hhexpense_t1
                     c_pov_likelihood_t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
     ushocks_exp_t1 |     0.0091             0.0080              0.0280
         revenue_t1 |     0.0160             0.0130              0.0370
          health_t1 |     0.0601             0.0579              0.1019
       hhexpense_t1 |     0.0046             0.0040              0.0120
c_pov_likelihood_t1 |     0.1023             0.0889              0.1019
------------------------------------------------------------------------------


Independent variable:  trt3
Outcome variables:   ushocks_exp_t1 revenue_t1 health_t1 hhexpense_t1
                     c_pov_likelihood_t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
     ushocks_exp_t1 |     0.6807             0.6224              0.9510
         revenue_t1 |     0.9554             0.9441              0.9840
          health_t1 |     0.8864             0.8861              0.9840
       hhexpense_t1 |     0.4508             0.4775              0.8362
c_pov_likelihood_t1 |     0.1730             0.1528              0.4815
------------------------------------------------------------------------------


Independent variable:  trt4
Outcome variables:   ushocks_exp_t1 revenue_t1 health_t1 hhexpense_t1
                     c_pov_likelihood_t1
Number of resamples: 1000


------------------------------------------------------------------------------
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
--------------------+---------------------------------------------------------
     ushocks_exp_t1 |     0.0290             0.0160              0.0739
         revenue_t1 |     0.0526             0.0480              0.1119
          health_t1 |     0.0567             0.0450              0.1119
       hhexpense_t1 |     0.0063             0.0050              0.0200
c_pov_likelihood_t1 |     0.8656             0.8551              0.8551
------------------------------------------------------------------------------



. **attrition bounds
. **1. [Lee Bounds]**
. foreach x of varlist trt2 trt3 trt4 {
  2.         leebounds ushocks_exp_t1 `x', level(95) cieffect tight() 
  3. }

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0447
Effect 95% conf. interval          : [-0.1063  0.0613]

------------------------------------------------------------------------------
ushocks_ex~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt2         |
       lower |  -.0537018   .0315162    -1.70   0.088    -.1154725    .0080688
       upper |  -.0069219   .0408856    -0.17   0.866    -.0870561    .0732123
------------------------------------------------------------------------------

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0209
Effect 95% conf. interval          : [-0.0514  0.0932]

------------------------------------------------------------------------------
ushocks_ex~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt3         |
       lower |   .0209779    .041016     0.51   0.609     -.059412    .1013678
       upper |   .0423315   .0288193     1.47   0.142    -.0141533    .0988163
------------------------------------------------------------------------------

Lee (2009) treatment effect bounds

Number of obs.                     =   990
Number of selected obs.            =   810
Trimming porportion                =   0.0252
Effect 95% conf. interval          : [-0.0971  0.0527]

------------------------------------------------------------------------------
ushocks_ex~1 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
trt4         |
       lower |  -.0435157   .0308575    -1.41   0.158    -.1039954    .0169639
       upper |  -.0176537    .040532    -0.44   0.663     -.097095    .0617876
------------------------------------------------------------------------------

. *
. /* dropped to save table space
> **2. [Behajel et al. Bounds]**
> foreach x of varlist ushocks_exp_t1 {
>         preserve
>                 display "`x'"
>                 gen itemA= `x' if trt2==1 & attempts<=3 
>                 egen iranklo_Aa =rank(itemA) if trt2==1, unique //from above
>                 egen iranklo_Ab =rank(-itemA) if trt2==1, unique //from belo
> w
>                 gen yupperA= `x'
>                 replace yupperA=. if (trt2==1 & iranklo_Aa<=20) | (trt2==1 &
>  attempts>3) //trim differences within 3 attempts and cut off all above 3-at
> tempts
>                 gen ylowerA= `x'
>                 replace ylowerA=. if (trt2==1 & iranklo_Ab<=20) | (trt2==1 &
>  attempts>3)
>                 reg ylowerA  trt2, r
>                 reg yupperA trt2, r
>         restore
> }
> *
> foreach x of varlist ushocks_exp_t1 {
>         preserve
>                 display "`x'"
>                 gen itemA= `x' if trt3==1 & attempts<=3 
>                 egen iranklo_Aa =rank(itemA) if trt3==1, unique //from above
>                 egen iranklo_Ab =rank(-itemA) if trt3==1, unique //from belo
> w
>                 gen yupperA= `x'
>                 replace yupperA=. if (trt3==1 & iranklo_Aa<=20) | (trt3==1 &
>  attempts>3) //trim differences within 3 attempts and cut off all above 3-at
> tempts
>                 gen ylowerA= `x'
>                 replace ylowerA=. if (trt3==1 & iranklo_Ab<=20) | (trt3==1 &
>  attempts>3)
>                 reg ylowerA  trt3, r
>                 reg yupperA trt3, r
>         restore
> }
> *
> 
> foreach x of varlist ushocks_exp_t1 {
>         preserve
>                 display "`x'"
>                 gen itemA= `x' if trt4==1 & attempts<=3 
>                 egen iranklo_Aa =rank(itemA) if trt4==1, unique //from above
>                 egen iranklo_Ab =rank(-itemA) if trt4==1, unique //from belo
> w
>                 gen yupperA= `x'
>                 replace yupperA=. if (trt4==1 & iranklo_Aa<=20) | (trt4==1 &
>  attempts>3) //trim differences within 3 attempts and cut off all above 3-at
> tempts
>                 gen ylowerA= `x'
>                 replace ylowerA=. if (trt4==1 & iranklo_Ab<=20) | (trt4==1 &
>  attempts>3)
>                 reg ylowerA  trt4, r
>                 reg yupperA trt4, r
>         restore
> }
> */
. *
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
end of do-file

. 
. ** Additional analysis
. do "$do_loc/_basel-analyze1.do" // quick

. /*
> Analyze baseline data
> 
> Input:
>         - 01_intermediate/Mkt_fieldData_census
>         
> Output:
>         - Figure B.9
> */
. 
. 
. ** Figure B.9 --------------------------------------------------------------
> --
. **Trust level for performing money transactions?
. use "$dta_loc_repl/01_intermediate/Mkt_fieldData_census", clear

. 
. sum c8q6, d //above median - preserve variance

       From a scale of 1 (low) to 5 (high) , indicate
              your level of trust for carrying 
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            2              1       Obs               1,995
25%            3              1       Sum of wgt.       1,995

50%            3                      Mean           3.168421
                        Largest       Std. dev.      .9562681
75%            4              5
90%            4              5       Variance       .9144486
95%            5              5       Skewness       -.582156
99%            5              5       Kurtosis        3.38162

. gen trustNo=(c8q6<=3)

. gen trustYes=(c8q6>3)

. tab trustNo 

    trustNo |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        779       37.93       37.93
          1 |      1,275       62.07      100.00
------------+-----------------------------------
      Total |      2,054      100.00

. tab trustYes

   trustYes |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,275       62.07       62.07
          1 |        779       37.93      100.00
------------+-----------------------------------
      Total |      2,054      100.00

. sum trustNo trustYes

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     trustNo |      2,054      .62074     .485321          0          1
    trustYes |      2,054      .37926     .485321          0          1

. ttesti 1275 0.62 0.48 779 0.37 0.48 //pval=0.000

Two-sample t test with equal variances
------------------------------------------------------------------------------
         |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       x |   1,275         .62    .0134427         .48    .5936278    .6463722
       y |     779         .37    .0171978         .48    .3362404    .4037596
---------+--------------------------------------------------------------------
Combined |   2,054     .525185    .0109217    .4949836    .5037662    .5466038
---------+--------------------------------------------------------------------
    diff |                 .25    .0218282                .2071923    .2928077
------------------------------------------------------------------------------
    diff = mean(x) - mean(y)                                      t =  11.4531
H0: diff = 0                                     Degrees of freedom =     2052

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. 
. // gr bar trustNo trustYes
. 
. graph hbar trustNo trustYes, bar(1, color(black)) bar(2, color(gs8)) nofill 
> asyvars ///
>  blabel(group, position(inside) format(%4.2f) box fcolor(white) lcolor(white
> )) ytitle("Trust in Transacting:  Share indicating no vs yes", size(small)) 
> blabel(bar) ///
>  legend(pos(7) row(1) stack label(1 "Trust=No") label(2 "Trust=Yes"))

. gr export "$output_loc/trust_transacting_graph.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/trust_transacting_graph.eps saved as EPS format

. 
. 
. 
. 
end of do-file

. do "$do_loc/_basel-analyze3.do" // quick

. /*
> Tables B.1, B.2, B.8
> 
> */
. 
. use "$dta_loc_repl/01_intermediate/repMkt_w_xtics", clear

. **Supply: merchant side
. **2a merchant xtics?
. **mfemale?
. sum mfemale

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     mfemale |      1,891    .3987308    .4897667          0          1

. local n = r(N)

. display `n'
1891

. local mean = r(mean)

. display `mean'
.39873083

. local sd = r(sd)

. display `sd'
.48976668

. sum mfemale if sample_repMkt==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     mfemale |        977     .419652    .4937547          0          1

. local nS = r(N)

. display `nS'
977

. local meanS = r(mean)

. display `meanS'
.419652

. local sdS = r(sd)

. display `sdS'
.49375473

. ttesti `n' `mean' `sd' `nS' `meanS' `sdS'

Two-sample t test with equal variances
------------------------------------------------------------------------------
         |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       x |   1,891    .3987308    .0112627    .4897667    .3766421    .4208195
       y |     977     .419652    .0157966    .4937547    .3886528    .4506512
---------+--------------------------------------------------------------------
Combined |   2,868    .4058577     .009171    .4911429    .3878753    .4238402
---------+--------------------------------------------------------------------
    diff |           -.0209212    .0193505               -.0588634    .0170211
------------------------------------------------------------------------------
    diff = mean(x) - mean(y)                                      t =  -1.0812
H0: diff = 0                                     Degrees of freedom =     2866

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.1399         Pr(|T| > |t|) = 0.2797          Pr(T > t) = 0.8601

. 
. **makan?
. sum makan

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       makan |      1,891    .5727129    .4948154          0          1

. local n = r(N)

. display `n'
1891

. local mean = r(mean)

. display `mean'
.57271285

. local sd = r(sd)

. display `sd'
.49481544

. sum makan if sample_repMkt==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       makan |        977    .5721597     .495019          0          1

. local nS = r(N)

. display `nS'
977

. local meanS = r(mean)

. display `meanS'
.57215967

. local sdS = r(sd)

. display `sdS'
.49501898

. ttesti `n' `mean' `sd' `nS' `meanS' `sdS'

Two-sample t test with equal variances
------------------------------------------------------------------------------
         |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       x |   1,891    .5727129    .0113788    .4948154    .5503965    .5950292
       y |     977    .5721597    .0158371     .495019    .5410811    .6032383
---------+--------------------------------------------------------------------
Combined |   2,868    .5725244    .0092393    .4947985    .5544081    .5906407
---------+--------------------------------------------------------------------
    diff |            .0005532    .0194985               -.0376793    .0387856
------------------------------------------------------------------------------
    diff = mean(x) - mean(y)                                      t =   0.0284
H0: diff = 0                                     Degrees of freedom =     2866

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.5113         Pr(|T| > |t|) = 0.9774          Pr(T > t) = 0.4887

. 
. **mage?
. sum mage

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
        mage |      1,891    26.29191    8.242838          0         50

. local n = r(N)

. display `n'
1891

. local mean = r(mean)

. display `mean'
26.291909

. local sd = r(sd)

. display `sd'
8.2428377

. sum mage if sample_repMkt==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
        mage |        977    27.17298    8.468466          0         50

. local nS = r(N)

. display `nS'
977

. local meanS = r(mean)

. display `meanS'
27.172979

. local sdS = r(sd)

. display `sdS'
8.4684665

. ttesti `n' `mean' `sd' `nS' `meanS' `sdS'

Two-sample t test with equal variances
------------------------------------------------------------------------------
         |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       x |   1,891    26.29191    .1895531    8.242838    25.92015    26.66366
       y |     977    27.17298    .2709302    8.468466    26.64131    27.70465
---------+--------------------------------------------------------------------
Combined |   2,868    26.59205    .1555333    8.329387    26.28708    26.89702
---------+--------------------------------------------------------------------
    diff |           -.8810695    .3278225               -1.523861   -.2382778
------------------------------------------------------------------------------
    diff = mean(x) - mean(y)                                      t =  -2.6876
H0: diff = 0                                     Degrees of freedom =     2866

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0036         Pr(|T| > |t|) = 0.0072          Pr(T > t) = 0.9964

. 
. **mEducAny?
. sum mEducAny

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    mEducAny |      1,891    .6916975    .4619144          0          1

. local n = r(N)

. display `n'
1891

. local mean = r(mean)

. display `mean'
.69169751

. local sd = r(sd)

. display `sd'
.46191438

. sum mEducAny if sample_repMkt==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    mEducAny |        977    .6847492    .4648536          0          1

. local nS = r(N)

. display `nS'
977

. local meanS = r(mean)

. display `meanS'
.68474923

. local sdS = r(sd)

. display `sdS'
.46485363

. ttesti `n' `mean' `sd' `nS' `meanS' `sdS'

Two-sample t test with equal variances
------------------------------------------------------------------------------
         |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       x |   1,891    .6916975    .0106222    .4619144     .670865      .71253
       y |     977    .6847492     .014872    .4648536    .6555645     .713934
---------+--------------------------------------------------------------------
Combined |   2,868    .6893305    .0086427    .4628484     .672384    .7062771
---------+--------------------------------------------------------------------
    diff |            .0069483     .018239               -.0288145    .0427111
------------------------------------------------------------------------------
    diff = mean(x) - mean(y)                                      t =   0.3810
H0: diff = 0                                     Degrees of freedom =     2866

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.6484         Pr(|T| > |t|) = 0.7033          Pr(T > t) = 0.3516

. **etc...
. 
. ** Table B.1 ---------------------------------------------------------------
> ----
. **Supply: select vendor xtics? married out...
. reg mfemale sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,840
                                                F(1, 129)         =       0.08
                                                Prob > F          =     0.7833
                                                R-squared         =     0.0005
                                                Root MSE          =     .49195

                     (Std. err. adjusted for 130 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
     mfemale | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |   .0210425    .076358     0.28   0.783    -.1300337    .1721187
       _cons |   .3986095   .0499621     7.98   0.000     .2997582    .4974608
------------------------------------------------------------------------------

. reg mmarried sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,840
                                                F(1, 129)         =       1.64
                                                Prob > F          =     0.2031
                                                R-squared         =     0.0093
                                                Root MSE          =     .43092

                     (Std. err. adjusted for 130 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
    mmarried | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |   .0835402   .0653045     1.28   0.203    -.0456663    .2127467
       _cons |   .2050985   .0433201     4.73   0.000     .1193886    .2908084
------------------------------------------------------------------------------

. reg makan sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,840
                                                F(1, 129)         =       0.00
                                                Prob > F          =     0.9906
                                                R-squared         =     0.0000
                                                Root MSE          =      .4951

                     (Std. err. adjusted for 130 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
       makan | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |   .0008966   .0760641     0.01   0.991     -.149598    .1513913
       _cons |    .571263   .0541032    10.56   0.000     .4642185    .6783076
------------------------------------------------------------------------------

. reg mage sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,840
                                                F(1, 129)         =       0.41
                                                Prob > F          =     0.5226
                                                R-squared         =     0.0023
                                                Root MSE          =     7.4262

                     (Std. err. adjusted for 130 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
        mage | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |   .7164316    1.11756     0.64   0.523    -1.494689    2.927552
       _cons |   26.45655   .5857575    45.17   0.000     25.29761    27.61548
------------------------------------------------------------------------------

. reg mEducAny sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,840
                                                F(1, 129)         =       0.28
                                                Prob > F          =     0.5952
                                                R-squared         =     0.0020
                                                Root MSE          =     .45638

                     (Std. err. adjusted for 130 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
    mEducAny | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -.0406274   .0762836    -0.53   0.595    -.1915564    .1103017
       _cons |   .7253766   .0501724    14.46   0.000     .6261093    .8246439
------------------------------------------------------------------------------

. reg mselfemployed sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,840
                                                F(1, 129)         =       2.82
                                                Prob > F          =     0.0957
                                                R-squared         =     0.0161
                                                Root MSE          =     .49602

                     (Std. err. adjusted for 130 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
mselfemplo~d | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -.1269298   .0756271    -1.68   0.096    -.2765598    .0227002
       _cons |   .5527231    .058144     9.51   0.000     .4376837    .6677624
------------------------------------------------------------------------------

. reg mselfIncome sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =        893
                                                F(1, 78)          =       2.98
                                                Prob > F          =     0.0883
                                                R-squared         =     0.0249
                                                Root MSE          =     1.4768

                      (Std. err. adjusted for 79 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
 mselfIncome | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -.4727816   .2739291    -1.73   0.088    -1.018132    .0725693
       _cons |   2.234801    .283914     7.87   0.000     1.669572     2.80003
------------------------------------------------------------------------------

. reg mbusTrained sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,839
                                                F(1, 129)         =       0.37
                                                Prob > F          =     0.5419
                                                R-squared         =     0.0019
                                                Root MSE          =     .49955

                     (Std. err. adjusted for 130 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
 mbusTrained | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |   .0432963    .070792     0.61   0.542    -.0967675    .1833601
       _cons |   .4930394   .0506239     9.74   0.000     .3928788    .5932001
------------------------------------------------------------------------------

. 
. **poverty?
. reg m4q1 sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,839
                                                F(1, 129)         =       2.77
                                                Prob > F          =     0.0986
                                                R-squared         =     0.0127
                                                Root MSE          =     8.7723

                     (Std. err. adjusted for 130 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
        m4q1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -1.990074   1.196332    -1.66   0.099    -4.357048    .3768986
       _cons |   17.54176   .8593952    20.41   0.000     15.84143     19.2421
------------------------------------------------------------------------------

. reg m4q2 sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,839
                                                F(1, 129)         =       0.16
                                                Prob > F          =     0.6944
                                                R-squared         =     0.0007
                                                Root MSE          =     1.8569

                     (Std. err. adjusted for 130 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
        m4q2 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -.0982992    .249601    -0.39   0.694    -.5921409    .3955425
       _cons |   2.574246   .2288694    11.25   0.000     2.121422     3.02707
------------------------------------------------------------------------------

. reg m4q3 sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,839
                                                F(1, 129)         =       0.21
                                                Prob > F          =     0.6473
                                                R-squared         =     0.0010
                                                Root MSE          =      1.624

                     (Std. err. adjusted for 130 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
        m4q3 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |    .102347   .2231563     0.46   0.647    -.3391731    .5438671
       _cons |   4.104408   .1636963    25.07   0.000     3.780531    4.428286
------------------------------------------------------------------------------

. reg m4q4 sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,839
                                                F(1, 129)         =       0.80
                                                Prob > F          =     0.3724
                                                R-squared         =     0.0050
                                                Root MSE          =     2.1628

                     (Std. err. adjusted for 130 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
        m4q4 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -.3066468    .342604    -0.90   0.372    -.9844972    .3712035
       _cons |   3.909513   .2225047    17.57   0.000     3.469282    4.349744
------------------------------------------------------------------------------

. reg m4q5 sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,839
                                                F(1, 129)         =       1.68
                                                Prob > F          =     0.1968
                                                R-squared         =     0.0098
                                                Root MSE          =     1.7543

                     (Std. err. adjusted for 130 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
        m4q5 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -.3490015   .2689895    -1.30   0.197    -.8812039    .1832009
       _cons |   4.617169   .1402744    32.92   0.000     4.339633    4.894706
------------------------------------------------------------------------------

. reg m4q6 sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,839
                                                F(1, 129)         =       6.07
                                                Prob > F          =     0.0150
                                                R-squared         =     0.0305
                                                Root MSE          =     6.0455

                     (Std. err. adjusted for 130 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
        m4q6 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -2.146732   .8711365    -2.46   0.015    -3.870297   -.4231673
       _cons |   16.35963   .6103053    26.81   0.000     15.15212    17.56713
------------------------------------------------------------------------------

. reg m4q7 sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,839
                                                F(1, 129)         =       2.62
                                                Prob > F          =     0.1082
                                                R-squared         =     0.0160
                                                Root MSE          =     1.1252

                     (Std. err. adjusted for 130 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
        m4q7 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -.2876698   .1778254    -1.62   0.108    -.6395017    .0641621
       _cons |   3.800464    .071531    53.13   0.000     3.658938     3.94199
------------------------------------------------------------------------------

. reg m4q8 sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,839
                                                F(1, 129)         =       3.10
                                                Prob > F          =     0.0805
                                                R-squared         =     0.0152
                                                Root MSE          =     .90808

                     (Std. err. adjusted for 130 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
        m4q8 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |   -.226243   .1284162    -1.76   0.080    -.4803176    .0278315
       _cons |   2.591647   .0784208    33.05   0.000      2.43649    2.746805
------------------------------------------------------------------------------

. reg m4q9 sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,839
                                                F(1, 129)         =       1.96
                                                Prob > F          =     0.1636
                                                R-squared         =     0.0083
                                                Root MSE          =     2.0047

                     (Std. err. adjusted for 130 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
        m4q9 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |   .3668054   .2617955     1.40   0.164    -.1511633    .8847742
       _cons |   8.466357   .2081138    40.68   0.000     8.054599    8.878115
------------------------------------------------------------------------------

. reg m4q10 sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,839
                                                F(1, 129)         =       2.05
                                                Prob > F          =     0.1542
                                                R-squared         =     0.0120
                                                Root MSE          =     3.2393

                     (Std. err. adjusted for 130 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
       m4q10 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |   .7156906   .4992978     1.43   0.154    -.2721823    1.703563
       _cons |   1.554524   .2876615     5.40   0.000      .985379     2.12367
------------------------------------------------------------------------------

. 
. 
. **2b transactions/ mkt size?
. *reg dailyNobCustomers sample_repMkt
. reg dailyTotMoney sample_repMkt

      Source |       SS           df       MS      Number of obs   =     1,839
-------------+----------------------------------   F(1, 1837)      =      0.02
       Model |  277398.754         1  277398.754   Prob > F        =    0.8902
    Residual |  2.6733e+10     1,837  14552667.1   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0005
       Total |  2.6734e+10     1,838  14544900.4   Root MSE        =    3814.8

------------------------------------------------------------------------------
dailyTotMo~y | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |   24.61173    178.263     0.14   0.890    -325.0077    374.2312
       _cons |   2296.046   129.9325    17.67   0.000     2041.216    2550.877
------------------------------------------------------------------------------

. reg dailyNobCustomers_nonM sample_repMkt

      Source |       SS           df       MS      Number of obs   =     1,409
-------------+----------------------------------   F(1, 1407)      =      0.00
       Model |  .200480794         1  .200480794   Prob > F        =    0.9924
    Residual |  3147911.92     1,407  2237.32191   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0007
       Total |  3147912.12     1,408  2235.73304   Root MSE        =      47.3

------------------------------------------------------------------------------
dailyNobCu~M | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -.0238599   2.520557    -0.01   0.992    -4.968315    4.920595
       _cons |   32.82973   1.796791    18.27   0.000     29.30505     36.3544
------------------------------------------------------------------------------

. reg dailyTotMoney_nonM sample_repMkt

      Source |       SS           df       MS      Number of obs   =     1,409
-------------+----------------------------------   F(1, 1407)      =      0.01
       Model |  185.953804         1  185.953804   Prob > F        =    0.9342
    Residual |  38368379.3     1,407  27269.6371   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0007
       Total |  38368565.3     1,408  27250.4015   Root MSE        =    165.14

------------------------------------------------------------------------------
dailyTotMo~M | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -.7266661   8.799785    -0.08   0.934    -17.98878    16.53544
       _cons |    156.404   6.272968    24.93   0.000     144.0987    168.7094
------------------------------------------------------------------------------

. 
. **joint, exclude main Y?
. reg sample_repMkt mfemale mmarried makan mage mEducAny mselfemployed mselfIn
> come mbusTrained, cluster(loccode)
note: mselfemployed omitted because of collinearity.

Linear regression                               Number of obs     =        892
                                                F(7, 78)          =       1.09
                                                Prob > F          =     0.3754
                                                R-squared         =     0.0640
                                                Root MSE          =     .48481

                      (Std. err. adjusted for 79 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
sample_rep~t | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     mfemale |   .0427829     .11959     0.36   0.721    -.1953025    .2808683
    mmarried |   .1945169   .1253384     1.55   0.125    -.0550127    .4440466
       makan |   .0248279   .1022418     0.24   0.809      -.17872    .2283758
        mage |  -.0006123    .007143    -0.09   0.932    -.0148328    .0136083
    mEducAny |   .0340466    .108964     0.31   0.756     -.182884    .2509773
mselfemplo~d |          0  (omitted)
 mselfIncome |  -.0464508   .0336465    -1.38   0.171     -.113436    .0205343
 mbusTrained |  -.0368098   .1145052    -0.32   0.749     -.264772    .1911525
       _cons |   .4644738   .2402422     1.93   0.057    -.0138116    .9427592
------------------------------------------------------------------------------

. test mfemale mmarried makan mage mEducAny mselfemployed mselfIncome mbusTrai
> ned

 ( 1)  mfemale = 0
 ( 2)  mmarried = 0
 ( 3)  makan = 0
 ( 4)  mage = 0
 ( 5)  mEducAny = 0
 ( 6)  o.mselfemployed = 0
 ( 7)  mselfIncome = 0
 ( 8)  mbusTrained = 0
       Constraint 6 dropped

       F(  7,    78) =    1.09
            Prob > F =    0.3754

. probit sample_repMkt mfemale mmarried makan mage mEducAny mselfemployed msel
> fIncome mbusTrained, cluster(loccode)

note: mselfemployed omitted because of collinearity.
Iteration 0:  Log pseudolikelihood = -616.26782  
Iteration 1:  Log pseudolikelihood = -586.98748  
Iteration 2:  Log pseudolikelihood = -586.93105  
Iteration 3:  Log pseudolikelihood = -586.93105  

Probit regression                                       Number of obs =    892
                                                        Wald chi2(7)  =   6.70
                                                        Prob > chi2   = 0.4602
Log pseudolikelihood = -586.93105                       Pseudo R2     = 0.0476

                      (Std. err. adjusted for 79 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
sample_rep~t | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     mfemale |   .1068769   .3127362     0.34   0.733    -.5060747    .7198286
    mmarried |    .506353    .326111     1.55   0.120    -.1328127    1.145519
       makan |   .0591692    .265764     0.22   0.824    -.4617187    .5800571
        mage |  -.0018478   .0186918    -0.10   0.921     -.038483    .0347874
    mEducAny |   .0955642   .2892779     0.33   0.741    -.4714101    .6625385
mselfemplo~d |          0  (omitted)
 mselfIncome |  -.1264005   .0917322    -1.38   0.168    -.3061923    .0533912
 mbusTrained |  -.0913074   .2976512    -0.31   0.759    -.6746931    .4920782
       _cons |  -.0776738    .622917    -0.12   0.901    -1.298569    1.143221
------------------------------------------------------------------------------

. test mfemale mmarried makan mage mEducAny mselfemployed mselfIncome mbusTrai
> ned

 ( 1)  [sample_repMkt]mfemale = 0
 ( 2)  [sample_repMkt]mmarried = 0
 ( 3)  [sample_repMkt]makan = 0
 ( 4)  [sample_repMkt]mage = 0
 ( 5)  [sample_repMkt]mEducAny = 0
 ( 6)  [sample_repMkt]o.mselfemployed = 0
 ( 7)  [sample_repMkt]mselfIncome = 0
 ( 8)  [sample_repMkt]mbusTrained = 0
       Constraint 6 dropped

           chi2(  7) =    6.70
         Prob > chi2 =    0.4602

. 
. 
. ** Table B.2 ---------------------------------------------------------------
> ----
. *3a Demand: customer xtics, same mkt?
. ** xtics? married out...
. reg cfemale sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,854
                                                F(1, 130)         =       0.01
                                                Prob > F          =     0.9397
                                                R-squared         =     0.0000
                                                Root MSE          =     .48364

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
     cfemale | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -.0020059   .0264621    -0.08   0.940    -.0543579    .0503461
       _cons |   .6289017   .0227031    27.70   0.000     .5839863    .6738172
------------------------------------------------------------------------------

. reg cmarried sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,854
                                                F(1, 130)         =       0.73
                                                Prob > F          =     0.3946
                                                R-squared         =     0.0004
                                                Root MSE          =     .49931

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
    cmarried | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |   .0210091    .024599     0.85   0.395    -.0276571    .0696754
       _cons |   .5179191   .0198116    26.14   0.000     .4787241     .557114
------------------------------------------------------------------------------

. reg cakan sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,854
                                                F(1, 130)         =       0.00
                                                Prob > F          =     0.9945
                                                R-squared         =     0.0000
                                                Root MSE          =      .4849

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
       cakan | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |    -.00027   .0394259    -0.01   0.995    -.0782695    .0777294
       _cons |   .6231214   .0365397    17.05   0.000     .5508319    .6954109
------------------------------------------------------------------------------

. reg cage sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,854
                                                F(1, 130)         =       3.59
                                                Prob > F          =     0.0604
                                                R-squared         =     0.0031
                                                Root MSE          =     15.031

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
        cage | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |   1.688732   .8914081     1.89   0.060    -.0748121    3.452276
       _cons |   38.63584   .7377514    52.37   0.000     37.17629    40.09539
------------------------------------------------------------------------------

. reg cEducAny sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,854
                                                F(1, 130)         =       0.33
                                                Prob > F          =     0.5643
                                                R-squared         =     0.0003
                                                Root MSE          =     .30621

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
    cEducAny | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |   .0097255   .0168274     0.58   0.564    -.0235656    .0430165
       _cons |   .8901734   .0159412    55.84   0.000     .8586357    .9217111
------------------------------------------------------------------------------

. reg cselfemployed sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,854
                                                F(1, 130)         =       0.75
                                                Prob > F          =     0.3887
                                                R-squared         =     0.0008
                                                Root MSE          =      .4667

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
cselfemplo~d | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |   .0257117   .0297284     0.86   0.389    -.0331024    .0845258
       _cons |    .665896    .029971    22.22   0.000      .606602      .72519
------------------------------------------------------------------------------

. reg cselfIncome sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,854
                                                F(1, 130)         =       3.09
                                                Prob > F          =     0.0811
                                                R-squared         =     0.0104
                                                Root MSE          =     .87417

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
 cselfIncome | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -.1793205   .1019922    -1.76   0.081    -.3810998    .0224588
       _cons |   1.478613   .1169204    12.65   0.000       1.2473    1.709926
------------------------------------------------------------------------------

. reg cMMoneyregistered sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,854
                                                F(1, 130)         =       0.01
                                                Prob > F          =     0.9416
                                                R-squared         =     0.0000
                                                Root MSE          =     .29402

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
cMMoneyreg~d | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -.0012589   .0171651    -0.07   0.942    -.0352181    .0327002
       _cons |   .9052023   .0148262    61.05   0.000     .8758704    .9345342
------------------------------------------------------------------------------

. 
. 
. **poverty?
. reg c2q1 sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,854
                                                F(1, 130)         =       3.42
                                                Prob > F          =     0.0667
                                                R-squared         =     0.0036
                                                Root MSE          =     8.6369

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
        c2q1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -1.035116   .5598435    -1.85   0.067    -2.142699    .0724677
       _cons |   16.36879   .5085637    32.19   0.000     15.36265    17.37492
------------------------------------------------------------------------------

. reg c2q2 sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,854
                                                F(1, 130)         =      10.97
                                                Prob > F          =     0.0012
                                                R-squared         =     0.0195
                                                Root MSE          =     1.6737

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
        c2q2 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |    .473432   .1429436     3.31   0.001     .1906352    .7562288
       _cons |   2.143353   .1422591    15.07   0.000      1.86191    2.424795
------------------------------------------------------------------------------

. reg c2q3 sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,854
                                                F(1, 130)         =       0.67
                                                Prob > F          =     0.4153
                                                R-squared         =     0.0008
                                                Root MSE          =     2.1359

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
        c2q3 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -.1245539   .1523987    -0.82   0.415    -.4260565    .1769487
       _cons |   3.428902   .1146622    29.90   0.000     3.202056    3.655747
------------------------------------------------------------------------------

. reg c2q4 sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,854
                                                F(1, 130)         =       1.94
                                                Prob > F          =     0.1657
                                                R-squared         =     0.0035
                                                Root MSE          =     2.2799

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
        c2q4 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -.2724244   .1954099    -1.39   0.166    -.6590195    .1141706
       _cons |    3.66474   .1963402    18.67   0.000     3.276304    4.053175
------------------------------------------------------------------------------

. reg c2q5 sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,854
                                                F(1, 130)         =      10.22
                                                Prob > F          =     0.0017
                                                R-squared         =     0.0228
                                                Root MSE          =     1.9099

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
        c2q5 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |    -.58459   .1828508    -3.20   0.002    -.9463385   -.2228415
       _cons |   4.372254   .1377094    31.75   0.000     4.099813    4.644696
------------------------------------------------------------------------------

. reg c2q6 sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,854
                                                F(1, 130)         =      14.56
                                                Prob > F          =     0.0002
                                                R-squared         =     0.0227
                                                Root MSE          =      5.658

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
        c2q6 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -1.728858   .4530525    -3.82   0.000    -2.625168   -.8325477
       _cons |   13.62775   .5377708    25.34   0.000     12.56383    14.69166
------------------------------------------------------------------------------

. reg c2q7 sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,854
                                                F(1, 130)         =      13.77
                                                Prob > F          =     0.0003
                                                R-squared         =     0.0158
                                                Root MSE          =     1.7392

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
        c2q7 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -.4416582   .1190206    -3.71   0.000    -.6771263   -.2061901
       _cons |        3.2   .1007582    31.76   0.000     3.000662    3.399338
------------------------------------------------------------------------------

. reg c2q8 sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,853
                                                F(1, 130)         =      13.44
                                                Prob > F          =     0.0004
                                                R-squared         =     0.0163
                                                Root MSE          =     1.2277

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
        c2q8 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -.3164716   .0863266    -3.67   0.000    -.4872585   -.1456848
       _cons |    2.07052   .0715957    28.92   0.000     1.928877    2.212164
------------------------------------------------------------------------------

. reg c2q9 sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,853
                                                F(1, 130)         =       1.00
                                                Prob > F          =     0.3191
                                                R-squared         =     0.0009
                                                Root MSE          =     2.6273

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
        c2q9 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -.1595423   .1595182    -1.00   0.319    -.4751298    .1560453
       _cons |   7.151445   .1236827    57.82   0.000     6.906754    7.396136
------------------------------------------------------------------------------

. reg c2q10 sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,853
                                                F(1, 130)         =       1.84
                                                Prob > F          =     0.1778
                                                R-squared         =     0.0019
                                                Root MSE          =     2.7575

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
       c2q10 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |   .2386815   .1761669     1.35   0.178    -.1098437    .5872067
       _cons |   1.180347   .1437873     8.21   0.000     .8958808    1.464813
------------------------------------------------------------------------------

. 
. **fraud?
. reg cfAttempts sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,853
                                                F(1, 130)         =       1.09
                                                Prob > F          =     0.2982
                                                R-squared         =     0.0018
                                                Root MSE          =     .49178

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
  cfAttempts | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -.0417226   .0399485    -1.04   0.298     -.120756    .0373107
       _cons |   .6115607   .0403417    15.16   0.000     .5317494     .691372
------------------------------------------------------------------------------

. reg _Xcfraud sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,853
                                                F(1, 130)         =       0.22
                                                Prob > F          =     0.6435
                                                R-squared         =     0.0002
                                                Root MSE          =     .45825

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
    _Xcfraud | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |   .0131825   .0284137     0.46   0.643    -.0430307    .0693956
       _cons |   .2924855   .0248331    11.78   0.000     .2433562    .3416149
------------------------------------------------------------------------------

. 
. *3b mkt, transactions?
. gen distToBank= c3q3a 
(1,486 missing values generated)

. gen walkTimeBank= c3q3b 
(1,486 missing values generated)

. gen bankUser = (c3q4==1)

. replace bankUser=. if missing(c3q4)
(1,486 real changes made, 1,486 to missing)

. 
. gen distTopostOffice = c3q7a
(1,802 missing values generated)

. gen walkTimepostOffice = c3q7b
(1,802 missing values generated)

. gen postOffUser=(c3q8==1)

. replace postOffUser=. if missing(c3q8)
(1,802 real changes made, 1,802 to missing)

. 
. gen distToMMoney= c4q2a
(154 missing values generated)

. gen walkTimeMMoney= c4q2b
(154 missing values generated)

. gen MMoneyUser=(c4q3==1)

. replace MMoneyUser=. if missing(c4q3)
(154 real changes made, 154 to missing)

. 
. reg distToBank sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =        425
                                                F(1, 73)          =       1.90
                                                Prob > F          =     0.1724
                                                R-squared         =     0.0092
                                                Root MSE          =     755.94

                      (Std. err. adjusted for 74 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
  distToBank | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |   147.8919   107.3145     1.38   0.172    -65.98555    361.7693
       _cons |   286.0791   73.10524     3.91   0.000     140.3805    431.7776
------------------------------------------------------------------------------

. reg distToMMoney sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,752
                                                F(1, 129)         =       0.68
                                                Prob > F          =     0.4102
                                                R-squared         =     0.0032
                                                Root MSE          =     94.502

                     (Std. err. adjusted for 130 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
distToMMoney | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -10.75883   13.02178    -0.83   0.410    -36.52273    15.00507
       _cons |   66.29504   12.78784     5.18   0.000     40.99399    91.59609
------------------------------------------------------------------------------

. 
. *reg wklyNobUsage sample_repMkt, cluster(loccode)
. reg wklyTotUseVol sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,752
                                                F(1, 129)         =       2.28
                                                Prob > F          =     0.1338
                                                R-squared         =     0.0014
                                                Root MSE          =     397.74

                     (Std. err. adjusted for 130 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
wklyTotUse~l | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |   29.28078   19.40628     1.51   0.134    -9.115027    67.67659
       _cons |   129.2273   12.98253     9.95   0.000     103.5411    154.9136
------------------------------------------------------------------------------

. reg wklyNobUsage_nonM sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,853
                                                F(1, 130)         =       0.30
                                                Prob > F          =     0.5833
                                                R-squared         =     0.0002
                                                Root MSE          =     15.023

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
wklyNobUsa~M | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |   .4304872   .7828372     0.55   0.583    -1.118263    1.979237
       _cons |   2.062428   .5310418     3.88   0.000     1.011825    3.113031
------------------------------------------------------------------------------

. reg wklyTotUseVol_nonM sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,853
                                                F(1, 130)         =       0.00
                                                Prob > F          =     0.9862
                                                R-squared         =     0.0000
                                                Root MSE          =     513.99

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
wklyTotUse~M | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -.4497402   25.95926    -0.02   0.986    -51.80703    50.90755
       _cons |   46.14913   24.14176     1.91   0.058    -1.612446    93.91071
------------------------------------------------------------------------------

. 
. *3c borrow + save behavior?
. gen likelyborrowMMoney =c5q1
(3 missing values generated)

. gen likelysaveMMoney =c5q5
(3 missing values generated)

. reg likelyborrowMMoney sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,853
                                                F(1, 130)         =       0.87
                                                Prob > F          =     0.3531
                                                R-squared         =     0.0014
                                                Root MSE          =     .87668

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
likelyborr~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |  -.0652021   .0699655    -0.93   0.353    -.2036204    .0732163
       _cons |   1.515607   .0736094    20.59   0.000      1.36998    1.661234
------------------------------------------------------------------------------

. reg likelysaveMMoney sample_repMkt, cluster(loccode)

Linear regression                               Number of obs     =      1,853
                                                F(1, 130)         =       0.00
                                                Prob > F          =     0.9654
                                                R-squared         =     0.0000
                                                Root MSE          =     1.2152

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
likelysave~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
sample_rep~t |   .0045552   .1046961     0.04   0.965    -.2025735     .211684
       _cons |   2.126012   .0952777    22.31   0.000     1.937516    2.314507
------------------------------------------------------------------------------

. /* 
> reg wklyNobBorrow sample_repMkt, cluster(loccode)
> reg wklyTotBorrowVol sample_repMkt, cluster(loccode)
> reg wklyNobSave sample_repMkt, cluster(loccode)
> reg wklyTotSaveVol sample_repMkt, cluster(loccode)
> */
. **joint, exclude main Y?
. reg sample_repMkt cfemale cmarried cakan cage cEducAny cselfemployed cselfIn
> come cMMoneyregistered, cluster(loccode)

Linear regression                               Number of obs     =      1,854
                                                F(8, 130)         =       1.45
                                                Prob > F          =     0.1819
                                                R-squared         =     0.0155
                                                Root MSE          =     .49621

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
sample_rep~t | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     cfemale |  -.0125122   .0290079    -0.43   0.667     -.069901    .0448765
    cmarried |   .0077196   .0262751     0.29   0.769    -.0442625    .0597017
       cakan |  -.0117681   .0420239    -0.28   0.780    -.0949072    .0713711
        cage |   .0020899   .0010324     2.02   0.045     .0000475    .0041323
    cEducAny |   .0479617   .0441392     1.09   0.279    -.0393624    .1352858
cselfemplo~d |   .0162088   .0327398     0.50   0.621    -.0485631    .0809806
 cselfIncome |  -.0607259   .0244263    -2.49   0.014    -.1090505   -.0124014
cMMoneyreg~d |   .0218328   .0468483     0.47   0.642     -.070851    .1145166
       _cons |   .4721903   .0828505     5.70   0.000     .3082804    .6361002
------------------------------------------------------------------------------

. test cfemale cmarried cakan cage cEducAny cselfemployed cselfIncome cMMoneyr
> egistered

 ( 1)  cfemale = 0
 ( 2)  cmarried = 0
 ( 3)  cakan = 0
 ( 4)  cage = 0
 ( 5)  cEducAny = 0
 ( 6)  cselfemployed = 0
 ( 7)  cselfIncome = 0
 ( 8)  cMMoneyregistered = 0

       F(  8,   130) =    1.45
            Prob > F =    0.1819

. probit sample_repMkt cfemale cakan cmarried cage cEducAny cselfemployed csel
> fIncome cMMoneyregistered, cluster(loccode)

Iteration 0:  Log pseudolikelihood = -1280.9451  
Iteration 1:  Log pseudolikelihood =  -1266.506  
Iteration 2:  Log pseudolikelihood = -1266.5004  
Iteration 3:  Log pseudolikelihood = -1266.5004  

Probit regression                                       Number of obs =  1,854
                                                        Wald chi2(8)  =  10.92
                                                        Prob > chi2   = 0.2060
Log pseudolikelihood = -1266.5004                       Pseudo R2     = 0.0113

                     (Std. err. adjusted for 131 clusters in loccode_sampling)
------------------------------------------------------------------------------
             |               Robust
sample_rep~t | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     cfemale |  -.0320646   .0738119    -0.43   0.664    -.1767334    .1126042
       cakan |  -.0309132   .1068092    -0.29   0.772    -.2402555     .178429
    cmarried |   .0198971   .0668676     0.30   0.766     -.111161    .1509551
        cage |   .0053323   .0026511     2.01   0.044     .0001363    .0105283
    cEducAny |   .1229934   .1123408     1.09   0.274    -.0971906    .3431774
cselfemplo~d |   .0406981    .083082     0.49   0.624    -.1221396    .2035357
 cselfIncome |  -.1550861   .0645442    -2.40   0.016    -.2815904   -.0285819
cMMoneyreg~d |   .0551392      .1184     0.47   0.641    -.1769205    .2871989
       _cons |  -.0709957   .2094462    -0.34   0.735    -.4815026    .3395113
------------------------------------------------------------------------------

. test cfemale cmarried cakan cage cEducAny cselfemployed cselfIncome cMMoneyr
> egistered

 ( 1)  [sample_repMkt]cfemale = 0
 ( 2)  [sample_repMkt]cmarried = 0
 ( 3)  [sample_repMkt]cakan = 0
 ( 4)  [sample_repMkt]cage = 0
 ( 5)  [sample_repMkt]cEducAny = 0
 ( 6)  [sample_repMkt]cselfemployed = 0
 ( 7)  [sample_repMkt]cselfIncome = 0
 ( 8)  [sample_repMkt]cMMoneyregistered = 0

           chi2(  8) =   10.92
         Prob > chi2 =    0.2060

. 
. 
. ** Table B.8 ---------------------------------------------------------------
> ----
. **summary statistics? paper 1
. **Vendors
. gen mhhsizeabove5=(m4q1<13)

. replace mhhsizeabove5=. if missing(m4q1)
(31 real changes made, 31 to missing)

. gen mhhhenglish=(m4q3==5)

. replace mhhhenglish=. if missing(m4q3)
(31 real changes made, 31 to missing)

. gen mwallcement=(m4q4==5)

. replace mwallcement=. if missing(m4q4)
(31 real changes made, 31 to missing)

. gen mhastoilet =(m4q5 >0)

. replace mhastoilet=. if missing(m4q5)
(31 real changes made, 31 to missing)

. gen mhasphones=(m4q9>0)

. replace mhasphones=. if missing(m4q9)
(31 real changes made, 31 to missing)

. gen mhasbicyle=(m4q10>0)

. replace mhasbicyle=. if missing(m4q10)
(31 real changes made, 31 to missing)

. 
. gen mbusexperience = m2q1a
(31 missing values generated)

. replace mbusexperience= m2q1b/12 if mbusexperience==0
(538 real changes made)

. gen motherbus=(m3q1==1)

. replace motherbus=. if missing(m3q1)
(31 real changes made, 31 to missing)

. 
. 
. tabstat mfemale mselfemployed mselfIncome mmarried makan mage mEducAny mbusT
> rained ///
>         mhhsizeabove5 mhhhenglish mwallcement mhastoilet mhasphones mhasbicy
> le ///
>         mbusexperience motherbus dailyTotMoney dailyNobCustomers_nonM dailyT
> otMoney_nonM ///
>         , by("") stat(mean sd) col(stat) long

    Variable |      Mean        SD
-------------+--------------------
     mfemale |  .3987308  .4897667
mselfemplo~d |  .4791116  .4996956
 mselfIncome |  2.014349  1.483915
    mmarried |  .2496034   .432898
       makan |  .5727129  .4948154
        mage |  26.29191  8.242838
    mEducAny |  .6916975  .4619144
 mbusTrained |  .5089947  .5000514
mhhsizeabo~5 |  .2238095  .4169062
 mhhhenglish |  .7698413  .4210457
 mwallcement |  .7492063  .4335847
  mhastoilet |  .8910053  .3117151
  mhasphones |  .9761905  .1524957
  mhasbicyle |  .2809524  .4495832
mbusexperi~e |  2.051455  2.127564
   motherbus |   .752381  .4317435
dailyTotMo~y |  2260.569  3775.947
dailyNobCu~M |  32.79184  47.06752
dailyTotMo~M |  155.1568  164.5743
----------------------------------

.  
. tabstat mselfemployed mselfIncome mmarried makan mage mEducAny mbusTrained /
> //
>         mhhsizeabove5 mhhhenglish mwallcement mhastoilet mhasphones mhasbicy
> le ///
>         mbusexperience motherbus dailyTotMoney dailyNobCustomers_nonM dailyT
> otMoney_nonM ///
>         , by(mfemale) stat(mean sd) col(stat) long

mfemale      Variable |      Mean        SD
----------------------+--------------------
0        mselfemplo~d |  .5057168  .5001873
          mselfIncome |   1.69913  1.254226
             mmarried |  .2348285  .4240781
                makan |  .5743184  .4946635
                 mage |  26.65259  8.493772
             mEducAny |  .6552331  .4755014
          mbusTrained |  .4982394  .5002171
         mhhsizeabo~5 |  .2306338   .421424
          mhhhenglish |   .784331  .4114668
          mwallcement |  .7508803  .4326938
           mhastoilet |  .8705986  .3357915
           mhasphones |  .9603873  .1951334
           mhasbicyle |   .340669  .4741429
         mbusexperi~e |  2.241711  2.275508
            motherbus |  .7517606  .4321817
         dailyTotMo~y |  2180.819  2757.953
         dailyNobCu~M |  37.22014  56.35111
         dailyTotMo~M |  167.6768  181.1946
----------------------+--------------------
1        mselfemplo~d |   .438992  .4965935
          mselfIncome |  2.561934  1.681882
             mmarried |  .2718833  .4452254
                makan |  .5702918   .495363
                 mage |  25.74801  7.823427
             mEducAny |  .7466844  .4351988
          mbusTrained |  .5251989  .4996961
         mhhsizeabo~5 |  .2135279  .4100692
          mhhhenglish |  .7480106  .4344434
          mwallcement |  .7466844  .4351988
           mhastoilet |  .9217507  .2687418
           mhasphones |         1         0
           mhasbicyle |  .1909814  .3933354
         mbusexperi~e |   1.76481  1.847645
            motherbus |  .7533156  .4313676
         dailyTotMo~y |  2380.723  4927.315
         dailyNobCu~M |   26.1338  26.47523
         dailyTotMo~M |  136.3327  133.7756
----------------------+--------------------
Total    mselfemplo~d |  .4791116  .4996956
          mselfIncome |  2.014349  1.483915
             mmarried |  .2496034   .432898
                makan |  .5727129  .4948154
                 mage |  26.29191  8.242838
             mEducAny |  .6916975  .4619144
          mbusTrained |  .5089947  .5000514
         mhhsizeabo~5 |  .2238095  .4169062
          mhhhenglish |  .7698413  .4210457
          mwallcement |  .7492063  .4335847
           mhastoilet |  .8910053  .3117151
           mhasphones |  .9761905  .1524957
           mhasbicyle |  .2809524  .4495832
         mbusexperi~e |  2.051455  2.127564
            motherbus |   .752381  .4317435
         dailyTotMo~y |  2260.569  3775.947
         dailyNobCu~M |  32.79184  47.06752
         dailyTotMo~M |  155.1568  164.5743
-------------------------------------------

. **Customers
. gen chhsizeabove5=(c2q1<13)

. replace chhsizeabove5=. if missing(c2q1)
(2 real changes made, 2 to missing)

. gen chhhenglish=(c2q3==5)

. replace chhhenglish=. if missing(c2q3)
(2 real changes made, 2 to missing)

. gen cwallcement=(c2q4==5)

. replace cwallcement=. if missing(m4q4)
(31 real changes made, 31 to missing)

. gen chastoilet =(c2q5 >0)

. replace chastoilet=. if missing(c2q5)
(2 real changes made, 2 to missing)

. gen chasphones=(c2q9>0)

. replace chasphones=. if missing(c2q9)
(3 real changes made, 3 to missing)

. gen chasbicyle=(c2q10>0)

. replace chasbicyle=. if missing(c2q10)
(3 real changes made, 3 to missing)

. 
. tabstat cfemale cselfemployed cselfIncome cmarried cakan cage cEducAny cMMon
> eyregistered ///
>         chhsizeabove5 chhhenglish cwallcement chastoilet chasphones chasbicy
> le ///
>         distToBank distTopostOffice distToMMoney bankUser postOffUser MMoney
> User ///
>         wklyTotUseVol wklyNobUsage_nonM wklyTotUseVol_nonM ///
>         likelyborrowMMoney likelysaveMMoney ///
>         cfAttempts _Xcfraud ///
>         , by("") stat(mean sd) col(stat) long

    Variable |      Mean        SD
-------------+--------------------
     cfemale |  .6237624  .4845671
cselfemplo~d |  .6810839  .4661779
 cselfIncome |  1.376238  .8683871
    cmarried |  .5351746  .4988912
       cakan |   .621678  .4850949
        cage |   39.5456  15.02157
    cEducAny |  .8968213  .3042715
cMMoneyreg~d |  .9051589  .2930717
chhsizeabo~5 |  .2449192  .4301514
 chhhenglish |  .6065659  .4886391
 cwallcement |   .705291  .4560325
  chastoilet |  .8494007  .3577511
  chasphones |  .9765381  .1514049
  chasbicyle |  .2148071  .4107956
  distToBank |   338.577  751.3704
distTopost~e |  382.9328  250.7379
distToMMoney |  61.28862  94.92809
    bankUser |  .8068966   .395188
 postOffUser |   .092437  .2908665
  MMoneyUser |  .9468025  .2244907
wklyTotUse~l |  144.1998  396.2836
wklyNobUsa~M |   2.27268  14.76621
wklyTotUse~M |  44.70021  505.1075
likelyborr~y |  1.477059  .8770209
likelysave~y |  2.112096  1.213491
  cfAttempts |  .5891554  .4921154
    _Xcfraud |  .2935349  .4555001
----------------------------------

. **Fraud-overcharged?
. tab c4q17, miss

   Have you |
  ever been |
overcharged |
    M-Money |
    fees at |
       cash |
    centers |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        390       20.30       20.30
          2 |      1,377       71.68       91.98
          . |        154        8.02      100.00
------------+-----------------------------------
      Total |      1,921      100.00

. 
. 
end of do-file

. do "$do_loc/_basel-analyze4.do" // quick

. /*
> Appendix material: 
>         - Figure B.6
>         - Table B.9
> */
. 
. 
. 
. use "$dta_loc_repl/01_intermediate/adminTransactData", clear

. 
. 
. 
. ** Figure B.6 --------------------------------------------------------------
> ----
. ciplot fYes_T, level(90) by(tranType) xlabel(, angle(55) labsize(small)) yli
> ne(0, lp(dash)) xline(22 30, lp(dash) lc(black)) ytitle("Probability (Miscon
> duct)", size(small)) xtitle("Transaction Group") note("")

. gr export "$output_loc/main_results/misconduct_yesB.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/main_results/misconduct_yesB.eps saved as EPS format

. ciplot sv_fAmt_T, level(90) by(tranType) xlabel(, angle(55) labsize(small)) 
> yline(0, lp(dash)) xline(22 30, lp(dash) lc(black)) ytitle("Amount-misconduc
> t (GHS)", size(small)) xtitle("Transaction Group") note("")

. gr export "$output_loc/main_results/misconduct_amtB.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/main_results/misconduct_amtB.eps saved as EPS format

. 
. 
. 
. ** Figure B.9 --------------------------------------------------------------
> ----
. tabstat fYes_T sv_fAmt_T, by(transactK) stat(mean sd) col(stat) long

transactK            Variable |      Mean        SD
------------------------------+--------------------
01 Cash-in GHS50       fYes_T |  .3513514  .4806512
                    sv_fAmt_T |  4.653846  1.093336
------------------------------+--------------------
02 Cash-in GHS16       fYes_T |       .52  .5029642
                    sv_fAmt_T |  4.076923  .2699528
------------------------------+--------------------
03 Cash-in GHS11       fYes_T |  .4821429  .5042031
                    sv_fAmt_T |  1.851852  1.406132
------------------------------+--------------------
04 Send GHS50 to       fYes_T |  .1818182  .3901537
                    sv_fAmt_T |    3.6875  1.624313
------------------------------+--------------------
05 Send GHS1100        fYes_T |  .1904762  .3974366
                    sv_fAmt_T |      3.25  1.982062
------------------------------+--------------------
06 Receive GHS50       fYes_T |        .2  .4058397
                    sv_fAmt_T |  2.714286   2.13809
------------------------------+--------------------
07 Receive GHS11       fYes_T |  .0882353  .2879022
                    sv_fAmt_T |  3.333333  2.081666
------------------------------+--------------------
08 Cash-in GHS50       fYes_T |  .0714286  .2593989
                    sv_fAmt_T |       3.2   2.04939
------------------------------+--------------------
09 Cash-in GHS16       fYes_T |  .0810811  .2748228
                    sv_fAmt_T |         2  1.549193
------------------------------+--------------------
10 Cash-out GHS5       fYes_T |  .0519481  .2233774
                    sv_fAmt_T |       2.5  1.290994
------------------------------+--------------------
11 Purchase new        fYes_T |   .326087  .4739596
                    sv_fAmt_T |  2.733333  1.099784
------------------------------+--------------------
12 Register new        fYes_T |  .0833333   .280306
                    sv_fAmt_T |         3  2.645751
------------------------------+--------------------
Total                  fYes_T |  .2277526  .4196988
                    sv_fAmt_T |  3.327815  1.591693
---------------------------------------------------

. tabstat fYes_T sv_fAmt_T, by(tranType) stat(mean sd) col(stat) long

tranType             Variable |      Mean        SD
------------------------------+--------------------
Falsification: 0       fYes_T |  .0678733  .2520994
                    sv_fAmt_T |  2.533333  1.641718
------------------------------+--------------------
OTC-base: 01-03        fYes_T |  .4487805  .4985872
                    sv_fAmt_T |  3.586957  1.498367
------------------------------+--------------------
OTC-token: 04-07       fYes_T |  .1677419  .3748481
                    sv_fAmt_T |      3.25  1.850676
------------------------------+--------------------
Open-account: 11       fYes_T |  .2195122  .4164634
                    sv_fAmt_T |  2.777778  1.352799
------------------------------+--------------------
Total                  fYes_T |  .2277526  .4196988
                    sv_fAmt_T |  3.327815  1.591693
---------------------------------------------------

. 
end of do-file

. do "$do_loc/_endl-analyze1.do" // quick

. /*
> Appendix figures: B.8
> 
> */
. 
. 
. use "$dta_loc_repl/00_Raw_anon/analyzed_EndlineAuditData.dta", clear

. 
. ** Figure B.8 --------------------------------------------------------------
> ----
. keep if trt==0 & _merge==3
(1,935 observations deleted)

. *bys xv_locality xv_vendor: keep if _n==1
. tab nq1

   As a vendor , how |
  IMPORTANT is it to |
you that show a high |
      degree of GOOD |
             MARKET  |      Freq.     Percent        Cum.
---------------------+-----------------------------------
  Slightly important |         60       17.86       17.86
Moderately Important |         12        3.57       21.43
           Important |        216       64.29       85.71
      Very Important |         48       14.29      100.00
---------------------+-----------------------------------
               Total |        336      100.00

. sum nq1, d //above median - preserve variance

     As a vendor , how IMPORTANT is it to you that show
                a high degree of GOOD MARKET 
-------------------------------------------------------------
      Percentiles      Smallest
 1%            2              2
 5%            2              2
10%            2              2       Obs                 336
25%            4              2       Sum of wgt.         336

50%            4                      Mean               3.75
                        Largest       Std. dev.      .9125984
75%            4              5
90%            5              5       Variance       .8328358
95%            5              5       Skewness      -.9027054
99%            5              5       Kurtosis       2.954908

. gen reputeNo=(nq1<=3)

. gen reputeYes=(nq1>3)

. tab reputeNo

   reputeNo |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        312       81.25       81.25
          1 |         72       18.75      100.00
------------+-----------------------------------
      Total |        384      100.00

. tab reputeYes

  reputeYes |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         72       18.75       18.75
          1 |        312       81.25      100.00
------------+-----------------------------------
      Total |        384      100.00

. sum reputeNo reputeYes

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    reputeNo |        384       .1875    .3908216          0          1
   reputeYes |        384       .8125    .3908216          0          1

. ttesti 384 0.19 0.39 384 0.81 0.39 //pval=0.000

Two-sample t test with equal variances
------------------------------------------------------------------------------
         |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       x |     384         .19    .0199021         .39    .1508689    .2291311
       y |     384         .81    .0199021         .39    .7708689    .8491311
---------+--------------------------------------------------------------------
Combined |     768          .5    .0179745    .4981235     .464715     .535285
---------+--------------------------------------------------------------------
    diff |                -.62    .0281458               -.6752521   -.5647479
------------------------------------------------------------------------------
    diff = mean(x) - mean(y)                                      t = -22.0281
H0: diff = 0                                     Degrees of freedom =      766

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. 
. gr bar reputeNo reputeYes

. 
. graph hbar reputeNo reputeYes, bar(1, color(black)) bar(2, color(gs8)) nofil
> l asyvars ///
>  blabel(group, position(inside) format(%4.2f) box fcolor(white) lcolor(white
> )) ytitle("Market Reputation Important:  Share indicating no vs yes", size(s
> mall)) blabel(bar) ///
>  legend(pos(7) row(1) stack label(1 "Reputation important=No") label(2 "Repu
> tation important=Yes"))

. gr export "$output_loc/reputation_important_graph.eps", replace
file
    /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replication_co
    > py/output/reputation_important_graph.eps saved as EPS format

. 
end of do-file

. 
. log close
      name:  <unnamed>
       log:  /Users/yazenkashlan/Documents/GitHub/annan2024_misconduct/replica
> tion_copy/annan2024_log.log
  log type:  text
 closed on:  27 Jul 2024, 15:03:55
------------------------------------------------------------------------------
